├── .DS_Store ├── assets ├── logo.png ├── main_page.png └── processed_logo.png ├── src ├── static │ ├── style.css │ ├── script.js │ ├── about.css │ ├── about.html │ ├── note_ai.css │ ├── static_feature_card.html │ ├── links.html │ ├── sidebar.css │ ├── main_chat_input.css │ ├── secondpage.css │ └── mian_area.css ├── utils │ ├── constants.py │ └── paths.py ├── config │ └── config_loader.py ├── models │ └── model_loader.py └── app.py ├── docs ├── install_ollama.txt ├── install_sqlite3.txt ├── install_c_compiler_llama_cpp.txt ├── raw_requirements.txt ├── setup.md └── langchain_tools.md ├── .gitignore ├── app └── style.css ├── raw_requirements.txt ├── README.md └── requirements.txt /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kuilenren/AI_Agent_Chatbot_Synapse/main/.DS_Store -------------------------------------------------------------------------------- /assets/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kuilenren/AI_Agent_Chatbot_Synapse/main/assets/logo.png -------------------------------------------------------------------------------- /assets/main_page.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kuilenren/AI_Agent_Chatbot_Synapse/main/assets/main_page.png -------------------------------------------------------------------------------- /assets/processed_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kuilenren/AI_Agent_Chatbot_Synapse/main/assets/processed_logo.png -------------------------------------------------------------------------------- /src/static/style.css: -------------------------------------------------------------------------------- 1 | /* static/style.css */ 2 | body { 3 | background-color: #f0f2f6; 4 | } 5 | 6 | h1 { 7 | color: #3a3a3a; 8 | text-align: center; 9 | font-family: 'Helvetica', sans-serif; 10 | } 11 | -------------------------------------------------------------------------------- /docs/install_ollama.txt: -------------------------------------------------------------------------------- 1 | curl -fsSL https://ollama.com/install.sh | sh 2 | 3 | # 0.4.1 -- current version 4 | # langchain-ollama -- 0.1.3 5 | # https://github.com/langchain-ai/langchain/commits/master/libs/partners/ollama/langchain_ollama/chat_models.py 6 | 7 | ollama run llama3.1:8b-instruct-q8_0 8 | 9 | /bye -------------------------------------------------------------------------------- /src/static/script.js: -------------------------------------------------------------------------------- 1 | // static/script.js 2 | document.addEventListener("DOMContentLoaded", function () { 3 | const heading = document.querySelector("h1"); 4 | if (heading) { 5 | heading.addEventListener("click", () => { 6 | alert("You clicked the heading!"); 7 | }); 8 | } 9 | }); 10 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .venv_agent 2 | *.db 3 | *.db-journal 4 | *.db-wal 5 | *.db-shm 6 | */credentials.yaml 7 | */df.csv 8 | *.gguf 9 | *.ggml 10 | *.toml 11 | *.sqlite3 12 | *.parquet 13 | *.feather 14 | backup_sentence_transformer 15 | all-MiniLM-L6-v2 16 | nomic-embed-text-v2-moe 17 | *.safetensors 18 | snowflake-arctic-embed-l-v2.0 19 | __pycache__ 20 | *.pkl 21 | *.index 22 | *.json 23 | *.xlsx 24 | *.csv 25 | *temp -------------------------------------------------------------------------------- /docs/install_sqlite3.txt: -------------------------------------------------------------------------------- 1 | # install SQLite version 3.47.0 on a Linux VM: 2 | sudo apt update 3 | sudo apt install build-essential wget 4 | wget https://www.sqlite.org/2024/sqlite-autoconf-3470000.tar.gz 5 | tar xvf sqlite-autoconf-3470000.tar.gz 6 | cd sqlite-autoconf-3470000 7 | ./configure 8 | make 9 | sudo make install 10 | sqlite3 --version 11 | cd .. 12 | rm -rf sqlite-autoconf-3470000 13 | rm sqlite-autoconf-3470000.tar.gz -------------------------------------------------------------------------------- /src/static/about.css: -------------------------------------------------------------------------------- 1 | .about-section-divider { 2 | height: 1.5px; 3 | background: linear-gradient(90deg, rgba(139, 148, 148, 0.01) 0%, rgba(139, 148, 148, 0.1) 50%, rgba(139, 148, 148, 0.01) 100%); 4 | margin: 15px 0; 5 | } 6 | 7 | .about-footer { 8 | font-size: 0.9rem; 9 | color: #666 important!; 10 | font-style: italic; 11 | line-height: 1.4; 12 | } 13 | 14 | .about-footer p { 15 | margin-bottom: 8px; 16 | color: #666 important!; 17 | } -------------------------------------------------------------------------------- /src/utils/constants.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict 2 | import os 3 | 4 | TASK_OPTIONS: List[str] = [ 5 | "Auto Selection", 6 | "Smart Search Agent", 7 | "Productivity Assistant", 8 | "Data Analysis Agent" 9 | ] 10 | 11 | DEFAULT_MODEL_PARAMS: Dict[str, any] = { 12 | "temperature": 0.1, #0.0, 13 | "top_k": 20, #15, 14 | "top_p": 0.4, #0.5, 15 | "repeat_penalty": 1.3, 16 | "n_ctx": 8192, 17 | "n_batch": 512, 18 | "n_gpu_layers": -1, 19 | "n_threads":os.cpu_count() 20 | } -------------------------------------------------------------------------------- /docs/install_c_compiler_llama_cpp.txt: -------------------------------------------------------------------------------- 1 | sudo apt-get install build-essential g++ clang 2 | sudo apt-get update 3 | sudo apt-get install build-essential cmake python3-dev 4 | 5 | # sudo find / -name nvcc 6 | 7 | export CUDACXX=/usr/local/cuda-12.5/bin/nvcc 8 | export PATH=/usr/local/cuda-12.5/bin:$PATH 9 | CMAKE_ARGS="-DGGML_CUDA=on -DCUDA_PATH=/usr/local/cuda-12.5 -DCUDAToolkit_ROOT=/usr/local/cuda-12.5 -DCUDAToolkit_INCLUDE_DIR=/usr/local/cuda-12.5/include -DCUDAToolkit_LIBRARY_DIR=/usr/local/cuda-12.5/lib64" FORCE_CMAKE=1 pip install llama-cpp-python==0.3.7 --no-cache-dir --force-reinstall 10 | 11 | # -DCMAKE_CUDA_ARCHITECTURES=80 -------------------------------------------------------------------------------- /app/style.css: -------------------------------------------------------------------------------- 1 | @import url('https://fonts.googleapis.com/css2?family=Lato:wght@300;400;700&display=swap'); 2 | 3 | html, body, [class*="css"], h5, h4, h3, p, span, .sidebar-text, .sidebar-subtext, .logo-text, code.st-emotion-cache-1drfmnm, .st-bm, .st-ak, .st-bn, .st-bo, .st-bp, .st-ar, .st-bq, .st-br, .st-bs, .st-emotion-cache-sy3zga, .stCode, 4 | .stTextInput, .stTextArea, .stButton, .stSelectbox, 5 | .stMultiSelect, .stNumber, .stMarkdownContainer { 6 | font-family: 'Lato', monospace !important; 7 | padding: -0.3px 0px !important; 8 | color: #ffffff; 9 | } 10 | 11 | .stSidebar { 12 | background-color: #202222; 13 | } 14 | -------------------------------------------------------------------------------- /src/static/about.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 |
5 |

6 | Explore AI Agents to generate responses tailored to your needs. 7 |

8 |
9 | 10 |
11 | 12 |
13 |

14 | The AI Agent is not a substitute for your expertise, but it can accelerate your analysis by generating text based on patterns in the data. 15 | When in doubt, always use your professional judgment. 16 |

17 |
18 |



19 |
20 | -------------------------------------------------------------------------------- /raw_requirements.txt: -------------------------------------------------------------------------------- 1 | # Ensure vs_BuildTools.exe is intalled with CMake checked and System Path setup 2 | langchain_community 3 | tf-keras 4 | langchain_huggingface 5 | langchain_experimental 6 | langchain_ollama 7 | faiss-cpu 8 | torch 9 | tensorflow 10 | accelerate 11 | peft 12 | bitsandbytes 13 | setuptools 14 | wheel 15 | cmake 16 | transformers 17 | sentence_transformers 18 | pyprojroot 19 | huggingface_hub 20 | # llama-cpp-python==0.2.90 21 | llama-cpp-python 22 | llamacpp 23 | pandas 24 | numpy 25 | tabulate 26 | pymysql 27 | langchain_groq 28 | langchain 29 | langchain_openai 30 | plotly 31 | nbformat 32 | seaborn 33 | streamlit 34 | streamlit_authenticator 35 | langgraph 36 | IPython 37 | openpyxl 38 | -------------------------------------------------------------------------------- /docs/raw_requirements.txt: -------------------------------------------------------------------------------- 1 | # Ensure vs_BuildTools.exe is intalled with CMake checked and System Path setup 2 | langchain_community 3 | tf-keras 4 | langchain_huggingface 5 | langchain_experimental 6 | langchain_ollama 7 | faiss-cpu 8 | torch 9 | tensorflow 10 | accelerate 11 | peft 12 | bitsandbytes 13 | setuptools 14 | wheel 15 | cmake 16 | transformers 17 | sentence_transformers 18 | pyprojroot 19 | huggingface_hub 20 | tensorflow 21 | # llama-cpp-python -> install as per /docs/install_c_compiler_llama_cpp.txt 22 | # llamacpp 23 | pandas 24 | numpy 25 | tabulate 26 | pymysql 27 | langchain_groq 28 | langchain==0.2.14 29 | langchain_openai 30 | plotly==5.24.0 31 | nbformat 32 | seaborn 33 | streamlit 34 | langgraph 35 | IPython 36 | openpyxl -------------------------------------------------------------------------------- /docs/setup.md: -------------------------------------------------------------------------------- 1 | # sudo apt install python3.12-venv 2 | python3.12 -m venv .venv_agent 3 | source .venv_agent/bin/activate 4 | 5 | 6 | # Manually download the ollama 7 | brew install ollama 8 | brew services start ollama 9 | 10 | # update ollama 11 | brew update 12 | brew upgrade ollama 13 | 14 | # Mac 15 | CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir 16 | 17 | 18 | # Windows 19 | $env:CMAKE_ARGS = "-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS" 20 | pip install llama-cpp-python 21 | 22 | 23 | pip install -r raw_requirements.txt 24 | pip freeze > requirements.txt 25 | 26 | ollama ps 27 | 28 | 29 | streamlit run src/app.py 30 | 31 | 32 | 33 | ---- 34 | 35 | ollama -v 36 | ollama version is 0.6.4 37 | 38 | pip show ollama 39 | Name: ollama 40 | Version: 0.4.7 41 | Summary: The official Python client for Ollama. -------------------------------------------------------------------------------- /src/static/note_ai.css: -------------------------------------------------------------------------------- 1 | /*Sidebar Note*/ 2 | .note-ai { 3 | position: fixed; 4 | bottom: 0; 5 | left: 20px; 6 | font-size: 0.85rem; 7 | color: #666; 8 | background: #191a1a !important; 9 | border-top: 1px solid rgba(221, 221, 221, 0.3); 10 | z-index: 999; 11 | padding: 0.7rem 1rem; 12 | width: 280px; /* Match sidebar width */ 13 | box-shadow: 0 -2px 8px rgba(0, 0, 0, 0.05); 14 | backdrop-filter: blur(8px); 15 | border-radius: 8px 8px 0 0; 16 | box-shadow: 2px 2px 2px #2f747a; 17 | transition: all 0.3s ease; 18 | } 19 | 20 | /* Adjust for expanded sidebar */ 21 | section[data-testid="stSidebar"][aria-expanded="true"] .note-ai { 22 | width: 280px; 23 | } 24 | 25 | @media screen and (max-width: 768px) { 26 | .note-ai { 27 | width: calc(85vw - 2rem); /* Match mobile sidebar width */ 28 | font-size: 0.8rem; 29 | padding: 0.6rem 1rem; 30 | } 31 | } 32 | 33 | 34 | -------------------------------------------------------------------------------- /src/static/static_feature_card.html: -------------------------------------------------------------------------------- 1 | 2 | 3 |
4 |
5 |
🌐
6 |
Smart Search Agent
7 |
Search the web in real time, summarize topics, and gather up-to-date information.
8 |
9 |
10 |
🗂️
11 |
Productivity Assistant
12 |
Automate daily tasks like email, calendar, and to-do lists.
13 |
14 |
15 |
📊
16 |
Data Analysis Agent
17 |
Analyze large datasets, generate visualizations, and provide insights.
18 |
19 |
-------------------------------------------------------------------------------- /src/static/links.html: -------------------------------------------------------------------------------- 1 |
2 |
3 | Documentation 4 | Github 5 | MLOps 6 | Finetuning 7 | About Me 8 | News 9 |
-------------------------------------------------------------------------------- /src/utils/paths.py: -------------------------------------------------------------------------------- 1 | # utils/paths.py 2 | import os 3 | from pathlib import Path 4 | from typing import Dict 5 | 6 | class PathManager: 7 | def __init__(self): 8 | # Base paths 9 | self.APP_DIR = Path("/Users/rajmaharajwala/raj-maharajwala-github/ai_agent") 10 | self.ASSETS_DIR = self.APP_DIR / "assets" 11 | self.MODELS_DIR = self.APP_DIR / "models" 12 | self.VECTORDB_DIR = self.APP_DIR / "index_vectordb" / "productivity" 13 | 14 | @property 15 | def model_paths(self) -> Dict[str, Path]: 16 | """Returns a dictionary of model paths""" 17 | return { 18 | "EMBEDDINGS": self.MODELS_DIR / "snowflake-arctic-embed-l-v2.0", 19 | "OLLAMA": "qwq:32b-q8_0" 20 | } 21 | 22 | @property 23 | def vectordb(self) -> Dict[str, Path]: 24 | """Returns a dictionary of vectordb paths""" 25 | return { 26 | "CSV_PATH": self.VECTORDB_DIR / "sql_examples_productivity.csv", 27 | "INDEX_PATH": self.VECTORDB_DIR / "sql-example-faiss-productivity-index-L2.index", 28 | "METADATA_DIR": self.VECTORDB_DIR / "sql-example-productivity-metadata.pkl" 29 | } 30 | 31 | @property 32 | def asset_paths(self) -> Dict[str, Path]: 33 | """Returns a dictionary of asset paths""" 34 | return { 35 | "LOGO": self.ASSETS_DIR / "logo.png", 36 | "CSS": self.ASSETS_DIR / "styles.css", 37 | # "CONFIG": self.ASSETS_DIR / "config.yaml" 38 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Synapse Workflows 2 | 3 | ## Overview 4 | Synapse Workflows is a platform that allows users to interact with specialized AI agents through natural language. The platform offers three main types of intelligent agents designed to assist with different tasks: 5 | 6 | ## UI Chatbot using Streamlit, HTML, CSS, and JavaScript 7 | ![alt text](./assets/main_page.png) 8 | 9 | 1. **Smart Search Agent** - Searches the web in real-time, summarizes topics, and gathers up-to-date information. 10 | 11 | 2. **Productivity Assistant** - Automates daily tasks including email management, calendar organization, and to-do lists. 12 | 13 | 3. **Data Analysis Agent** - Analyzes large datasets, generates visualizations, and provides insights. 14 | 15 | ## Features 16 | - Intuitive natural language interface 17 | - Multiple specialized AI agents for different use cases 18 | - Real-time web search capabilities 19 | - Task automation for productivity 20 | - Data analysis and visualization tools 21 | 22 | ## Getting Started 23 | Users can begin by selecting one of the three available agents from the main interface or by typing queries directly into the "Ask Anything..." input field. 24 | 25 | ## Notes 26 | - The platform indicates that AI workflows may occasionally produce unexpected results. 27 | - The interface includes a "Deploy" button in the upper right for deployment options. 28 | 29 | ## Support 30 | For more detailed information, users can access the documentation section through the link provided at the bottom of the interface. -------------------------------------------------------------------------------- /src/config/config_loader.py: -------------------------------------------------------------------------------- 1 | # src/config/config_loader.py 2 | import os 3 | import yaml 4 | import streamlit_authenticator as stauth 5 | from typing import Dict, List, Optional 6 | 7 | class ConfigLoader: 8 | def __init__(self, config_dir: str = '/mnt/data/ai_chatbot/code.acrivon/AP3-Chatbot/config'): 9 | self.config_dir = config_dir 10 | self.configs = {} 11 | self.authenticator = None 12 | 13 | def load_config(self, yaml_files: List[str]) -> Dict: 14 | """Load multiple YAML configuration files.""" 15 | for yaml_file in yaml_files: 16 | config_path = os.path.join(self.config_dir, f'{yaml_file}.yaml') 17 | try: 18 | with open(config_path, 'r') as file: 19 | self.configs[yaml_file] = yaml.safe_load(file) 20 | except Exception as e: 21 | print(f"Error loading config {yaml_file}: {e}") 22 | self.configs[yaml_file] = {} 23 | return self.configs 24 | 25 | def get_config(self, config_name: str) -> Dict: 26 | """Get specific configuration by name.""" 27 | return self.configs.get(config_name, {}) 28 | 29 | def init_authentication(self) -> Optional[stauth.Authenticate]: 30 | """Initialize authentication with proper configuration""" 31 | try: 32 | config_path = os.path.join(self.config_dir, 'auth.yaml') 33 | if not os.path.exists(config_path): 34 | raise FileNotFoundError(f"Auth config not found at {config_path}") 35 | 36 | with open(config_path, 'r') as file: 37 | config = yaml.safe_load(file) 38 | 39 | # Initialize authenticator with required parameters 40 | authenticator = stauth.Authenticate( 41 | credentials=config_path, 42 | cookie_name=config['cookie']['name'], 43 | key=config['cookie']['key'], 44 | cookie_expiry_days=config['cookie']['expiry_days'], 45 | auto_hash = True 46 | ) 47 | return authenticator 48 | 49 | except Exception as e: 50 | print(f"Authentication initialization error: {e}") 51 | return None -------------------------------------------------------------------------------- /docs/langchain_tools.md: -------------------------------------------------------------------------------- 1 | ## Search​ 2 | ​ 3 | The following table shows tools that execute online searches in some shape or form: 4 | [Bing Search](https://python.langchain.com/docs/integrations/tools/bing_search/) 5 | [Brave Search](https://python.langchain.com/docs/integrations/tools/brave_search/) 6 | [DuckDuckgoSearch](https://python.langchain.com/docs/integrations/tools/ddg/) 7 | [Exa Search](https://python.langchain.com/docs/integrations/tools/exa_search/) 8 | [Google Search](https://python.langchain.com/docs/integrations/tools/google_search/) 9 | [Google Serper](https://python.langchain.com/docs/integrations/tools/google_serper/) 10 | [Jina Search](https://python.langchain.com/docs/integrations/tools/jina_search/) 11 | [Mojeek Search](https://python.langchain.com/docs/integrations/tools/mojeek_search/) 12 | [SearchApi](https://python.langchain.com/docs/integrations/tools/searchapi/) 13 | [SearxNG Search](https://python.langchain.com/docs/integrations/tools/searx_search/) 14 | [SerpAPI](https://python.langchain.com/docs/integrations/tools/serpapi/) 15 | [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search/) 16 | [You.com Search](https://python.langchain.com/docs/integrations/tools/you/) 17 | 18 | ## Code Interpreter​ 19 | ​ 20 | The following table shows tools that can be used as code interpreters: 21 | [Azure Container Apps dynamic sessions](https://python.langchain.com/docs/integrations/tools/azure_dynamic_sessions/) 22 | [Bearly Code Interpreter](https://python.langchain.com/docs/integrations/tools/bearly/) 23 | [Riza Code Interpreter](https://python.langchain.com/docs/integrations/tools/riza/) 24 | 25 | ## Productivity​ 26 | ​ 27 | The following table shows tools that can be used to automate tasks in productivity tools: 28 | [Github Toolkit](https://python.langchain.com/docs/integrations/tools/github/) 29 | [Gitlab Toolkit](https://python.langchain.com/docs/integrations/tools/gitlab/) 30 | [Gmail Toolkit](https://python.langchain.com/docs/integrations/tools/gmail/) 31 | [Infobip Tool](https://python.langchain.com/docs/integrations/tools/infobip/) 32 | [Jira Toolkit](https://python.langchain.com/docs/integrations/tools/jira/) 33 | [rate limits](https://developer.atlassian.com/cloud/jira/platform/rate-limiting/) 34 | [Office365 Toolkit](https://python.langchain.com/docs/integrations/tools/office365/) 35 | [rate limits](https://learn.microsoft.com/en-us/graph/throttling-limits) 36 | [Slack Toolkit](https://python.langchain.com/docs/integrations/tools/slack/) 37 | [Twilio Tool](https://python.langchain.com/docs/integrations/tools/twilio/) 38 | [pay-as-you-go pricing](https://www.twilio.com/en-us/pricing) 39 | 40 | ## Web Browsing​ 41 | ​ 42 | The following table shows tools that can be used to automate tasks in web browsers: 43 | [AgentQL Toolkit](https://python.langchain.com/docs/integrations/tools/agentql/) 44 | [Hyperbrowser Browser Agent Tools](https://python.langchain.com/docs/integrations/tools/hyperbrowser_browser_agent_tools/) 45 | [Hyperbrowser Web Scraping Tools](https://python.langchain.com/docs/integrations/tools/hyperbrowser_web_scraping_tools/) 46 | [MultiOn Toolkit](https://python.langchain.com/docs/integrations/tools/multion/) 47 | [PlayWright Browser Toolkit](https://python.langchain.com/docs/integrations/tools/playwright/) 48 | [Requests Toolkit](https://python.langchain.com/docs/integrations/tools/requests/) 49 | 50 | ## Database​ 51 | ​ 52 | The following table shows tools that can be used to automate tasks in databases: 53 | [Cassandra Database Toolkit](https://python.langchain.com/docs/integrations/tools/cassandra_database/) 54 | [SQLDatabase Toolkit](https://python.langchain.com/docs/integrations/tools/sql_database/) 55 | [Spark SQL Toolkit](https://python.langchain.com/docs/integrations/tools/spark_sql/) 56 | 57 | ## Finance​ 58 | ​ 59 | The following table shows tools that can be used to execute financial transactions such as payments, purchases, and more: 60 | [GOAT](https://python.langchain.com/docs/integrations/tools/goat/) -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==2.2.2 2 | accelerate==1.6.0 3 | aiohappyeyeballs==2.6.1 4 | aiohttp==3.11.18 5 | aiosignal==1.3.2 6 | altair==5.5.0 7 | annotated-types==0.7.0 8 | anyio==4.9.0 9 | asttokens==3.0.0 10 | astunparse==1.6.3 11 | attrs==25.3.0 12 | bcrypt==4.3.0 13 | bitsandbytes==0.42.0 14 | blinker==1.9.0 15 | cachetools==5.5.2 16 | captcha==0.7.1 17 | certifi==2025.1.31 18 | cffi==1.17.1 19 | charset-normalizer==3.4.1 20 | click==8.1.8 21 | cmake==4.0.0 22 | contourpy==1.3.2 23 | cryptography==44.0.2 24 | cycler==0.12.1 25 | dataclasses-json==0.6.7 26 | decorator==5.2.1 27 | diskcache==5.6.3 28 | distro==1.9.0 29 | et_xmlfile==2.0.0 30 | executing==2.2.0 31 | extra-streamlit-components==0.1.80 32 | faiss-cpu==1.10.0 33 | fastjsonschema==2.21.1 34 | filelock==3.18.0 35 | flatbuffers==25.2.10 36 | fonttools==4.57.0 37 | frozenlist==1.6.0 38 | fsspec==2025.3.2 39 | gast==0.6.0 40 | gitdb==4.0.12 41 | GitPython==3.1.44 42 | google-pasta==0.2.0 43 | groq==0.23.0 44 | grpcio==1.71.0 45 | h11==0.14.0 46 | h5py==3.13.0 47 | httpcore==1.0.8 48 | httpx==0.28.1 49 | httpx-sse==0.4.0 50 | huggingface-hub==0.30.2 51 | idna==3.10 52 | ipython==9.1.0 53 | ipython_pygments_lexers==1.1.1 54 | jedi==0.19.2 55 | Jinja2==3.1.6 56 | jiter==0.9.0 57 | joblib==1.4.2 58 | jsonpatch==1.33 59 | jsonpointer==3.0.0 60 | jsonschema==4.23.0 61 | jsonschema-specifications==2024.10.1 62 | jupyter_core==5.7.2 63 | keras==3.9.2 64 | kiwisolver==1.4.8 65 | langchain==0.3.24 66 | langchain-community==0.3.22 67 | langchain-core==0.3.55 68 | langchain-experimental==0.3.4 69 | langchain-groq==0.3.2 70 | langchain-huggingface==0.1.2 71 | langchain-ollama==0.3.2 72 | langchain-openai==0.3.14 73 | langchain-text-splitters==0.3.8 74 | langgraph==0.3.31 75 | langgraph-checkpoint==2.0.24 76 | langgraph-prebuilt==0.1.8 77 | langgraph-sdk==0.1.63 78 | langsmith==0.3.33 79 | libclang==18.1.1 80 | llama_cpp_python==0.3.8 81 | llamacpp==0.1.14 82 | Markdown==3.8 83 | markdown-it-py==3.0.0 84 | MarkupSafe==3.0.2 85 | marshmallow==3.26.1 86 | matplotlib==3.10.1 87 | matplotlib-inline==0.1.7 88 | mdurl==0.1.2 89 | ml_dtypes==0.5.1 90 | mpmath==1.3.0 91 | multidict==6.4.3 92 | mypy_extensions==1.1.0 93 | namex==0.0.9 94 | narwhals==1.35.0 95 | nbformat==5.10.4 96 | networkx==3.4.2 97 | numpy==2.1.3 98 | ollama==0.4.8 99 | openai==1.75.0 100 | openpyxl==3.1.5 101 | opt_einsum==3.4.0 102 | optree==0.15.0 103 | orjson==3.10.16 104 | ormsgpack==1.9.1 105 | packaging==24.2 106 | pandas==2.2.3 107 | parso==0.8.4 108 | peft==0.15.2 109 | pexpect==4.9.0 110 | pillow==11.2.1 111 | platformdirs==4.3.7 112 | plotly==6.0.1 113 | prompt_toolkit==3.0.51 114 | propcache==0.3.1 115 | protobuf==5.29.4 116 | psutil==7.0.0 117 | ptyprocess==0.7.0 118 | pure_eval==0.2.3 119 | pyarrow==19.0.1 120 | pycparser==2.22 121 | pydantic==2.11.3 122 | pydantic-settings==2.9.1 123 | pydantic_core==2.33.1 124 | pydeck==0.9.1 125 | Pygments==2.19.1 126 | PyJWT==2.10.1 127 | PyMySQL==1.1.1 128 | pyparsing==3.2.3 129 | pyprojroot==0.3.0 130 | python-dateutil==2.9.0.post0 131 | python-dotenv==1.1.0 132 | pytz==2025.2 133 | PyYAML==6.0.2 134 | referencing==0.36.2 135 | regex==2024.11.6 136 | requests==2.32.3 137 | requests-toolbelt==1.0.0 138 | rich==14.0.0 139 | rpds-py==0.24.0 140 | safetensors==0.5.3 141 | scikit-learn==1.6.1 142 | scipy==1.15.2 143 | seaborn==0.13.2 144 | sentence-transformers==4.1.0 145 | setuptools==79.0.0 146 | six==1.17.0 147 | smmap==5.0.2 148 | sniffio==1.3.1 149 | SQLAlchemy==2.0.40 150 | stack-data==0.6.3 151 | streamlit==1.44.1 152 | streamlit-authenticator==0.4.2 153 | sympy==1.13.1 154 | tabulate==0.9.0 155 | tenacity==9.1.2 156 | tensorboard==2.19.0 157 | tensorboard-data-server==0.7.2 158 | tensorflow==2.19.0 159 | termcolor==3.0.1 160 | tf_keras==2.19.0 161 | threadpoolctl==3.6.0 162 | tiktoken==0.9.0 163 | tokenizers==0.21.1 164 | toml==0.10.2 165 | torch==2.6.0 166 | tornado==6.4.2 167 | tqdm==4.67.1 168 | traitlets==5.14.3 169 | transformers==4.51.3 170 | typing-inspect==0.9.0 171 | typing-inspection==0.4.0 172 | typing_extensions==4.13.2 173 | tzdata==2025.2 174 | urllib3==2.4.0 175 | wcwidth==0.2.13 176 | Werkzeug==3.1.3 177 | wheel==0.45.1 178 | wrapt==1.17.2 179 | xxhash==3.5.0 180 | yarl==1.20.0 181 | zstandard==0.23.0 182 | -------------------------------------------------------------------------------- /src/models/model_loader.py: -------------------------------------------------------------------------------- 1 | from llama_cpp import Llama 2 | # from ollama_functions_custom import OllamaFunctions 3 | from typing import Optional, Dict 4 | from utils.paths import PathManager # CUSTOM_CLASS 5 | from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler 6 | from langchain_openai import ChatOpenAI 7 | # from langchain_groq import ChatGroq 8 | from langchain_ollama import ChatOllama 9 | from langchain_ollama.llms import OllamaLLM 10 | import torch 11 | import os 12 | 13 | class ModelLoader: 14 | def __init__(self, configs: Dict): 15 | self.configs = configs 16 | self.path_manager = PathManager() 17 | self.model_paths = self.path_manager.model_paths 18 | def load_model(self, task: str) -> any: 19 | """Load appropriate model based on task and parameters.""" 20 | 21 | if task == 'Productivity Assistant': 22 | return self._load_reasoning( 23 | model_path = str(self.model_paths["OLLAMA_Deepseek-R1"]), 24 | base_url = 'http://127.0.0.1:11434' 25 | # **model_params 26 | ) 27 | 28 | elif task == 'Auto Selection': 29 | self._load_default_ollama( 30 | # base_url=self.configs['model']['end_point_local'], 31 | model_path = str(self.model_paths["OLLAMA_QWEN"]), 32 | base_url = 'http://127.0.0.1:11434' 33 | ) 34 | 35 | return self._load_default_ollama( 36 | # base_url=self.configs['model']['end_point_local'], 37 | model_path = str(self.model_paths["OLLAMA_QWEN"]), 38 | base_url = 'http://127.0.0.1:11434' 39 | # **model_params 40 | ) 41 | 42 | def _load_reasoning(self, model_path: str, base_url: str) -> OllamaLLM: # **kwargs 43 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 44 | if torch.cuda.is_available(): 45 | torch.cuda.empty_cache() 46 | 47 | return OllamaLLM( 48 | base_url=base_url, 49 | model=model_path, 50 | top_k=20, 51 | top_p=0.4, 52 | temperature=0.0, 53 | num_ctx = 29184, 54 | verbose=True, 55 | callbacks=[StreamingStdOutCallbackHandler()], 56 | streaming=True, 57 | seed = -1, 58 | num_gpu=-2, # Explicitly use one device GPU (Total - 1) 59 | keep_alive=-1, 60 | f16=True, 61 | # mirostat=1, # Added adaptive sampling for better quality 62 | # mirostat_tau=4.0, # Conservative tau value for factual responses 63 | # mirostat_eta=0.1 # Learning rate for mirostat 64 | ) 65 | 66 | def _load_default_ollama(self, model_path: str, base_url: str) -> ChatOllama: # , **kwargs 67 | os.environ["CUDA_VISIBLE_DEVICES"] = "1" 68 | return ChatOllama( 69 | base_url=base_url, 70 | model=model_path, 71 | top_k=30, # Increased from 20 to consider more token candidates 72 | top_p=0.4, 73 | temperature=0.03, # Reduced slightly for more deterministic/factual responses 74 | num_ctx=15000,#19184, # Increased context window to maximum supported 75 | verbose=False, 76 | format='json', 77 | num_gpu=-2, # Explicitly use one device GPU (Total - 1) 78 | keep_alive=-1, 79 | seed=42, # Fixed seed for reproducibility 80 | f16=True, 81 | mirostat=1, # Added adaptive sampling for better quality 82 | mirostat_tau=4.0, # Conservative tau value for factual responses 83 | mirostat_eta=0.1 # Learning rate for mirostat 84 | ) 85 | 86 | ############### 87 | """ 88 | sudo systemctl stop ollama 89 | sudo nano /etc/systemd/system/ollama.service 90 | [Service] 91 | Environment="OLLAMA_FLASH_ATTENTION=1" 92 | Environment="OLLAMA_KV_CACHE_TYPE=q8_0" 93 | Environment="OLLAMA_MAX_LOADED_MODELS=3" 94 | Environment="OLLAMA_NUM_PARALLEL=4" 95 | Environment="OLLAMA_NUMA=1" 96 | # Environment="OLLAMA_KEEP_ALIVE=-1" 97 | sudo systemctl daemon-reload 98 | sudo systemctl restart ollama 99 | --- 100 | torch.cuda.empty_cache() 101 | --- 102 | sudo journalctl -u ollama.service | grep "kv_cache" 103 | --- 104 | sudo systemctl stop ollama 105 | --- 106 | OLLAMA_KEEP_ALIVE=-1 OLLAMA_FLASH_ATTENTION=true OLLAMA_KV_CACHE_TYPE=q8_0 OLLAMA_MAX_LOADED_MODELS=3 ollama serve 107 | (or) 108 | export OLLAMA_MAX_LOADED_MODELS=3 109 | export OLLAMA_NUM_PARALLEL=4 110 | export OLLAMA_FLASH_ATTENTION=true 111 | export OLLAMA_KV_CACHE_TYPE=q8_0 112 | ollama serve 113 | --- 114 | nvidia-smi -l 1 115 | --- 116 | """ 117 | ##### -------------------------------------------------------------------------------- /src/static/sidebar.css: -------------------------------------------------------------------------------- 1 | @keyframes rotate-y { 2 | 0% { transform: rotateY(0deg); } 3 | 100% { transform: rotateY(360deg); } 4 | } 5 | 6 | @keyframes rotate-z { 7 | 0% { transform: rotateZ(0deg); } 8 | 100% { transform: rotateZ(360deg); } 9 | } 10 | 11 | .rotating-logo { 12 | width: 60px; 13 | height: 64px; 14 | transform-style: preserve-3d; 15 | z-index: 9999; 16 | overflow: hidden; 17 | display: inline-block; 18 | vertical-align: middle; 19 | margin: -60px 5px 0 10px; 20 | transition: transform 0.3s ease, filter 0.3s ease; 21 | border-radius: 12px; 22 | } 23 | 24 | .rotating-logo:hover { 25 | animation: rotate-z 8s infinite cubic-bezier(0.68, -0.55, 0.265, 1.55); 26 | filter: drop-shadow(0px 4px 8px rgba(0, 0, 0, 0.15)); 27 | transform: scale(1.05); 28 | } 29 | 30 | .sidebar-text { 31 | color: #e0e0e0; 32 | margin-left: -18px; 33 | text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.3); 34 | font-size: clamp(1.4rem, 1.2vw + 0.8rem, 1.7rem); 35 | font-weight: 700; 36 | padding: 0.6em 0.8em; 37 | display: inline-block; 38 | vertical-align: middle; 39 | margin-top: -68px; 40 | white-space: nowrap; 41 | overflow: hidden; 42 | transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); 43 | text-overflow: ellipsis; 44 | will-change: transform; 45 | text-shadow: 1px 1px 2px rgba(255, 255, 255, 0.8); 46 | letter-spacing: -0.02em; 47 | } 48 | 49 | section[data-testid="stSidebar"][aria-expanded="true"] { 50 | position: relative; 51 | height: 100vh; 52 | overflow: hidden; 53 | min-width: 280px; 54 | max-width: 320px; 55 | background: #191a1a; 56 | box-shadow: 0 4px 20px rgba(0, 0, 0, 0.2); 57 | border-right: 1px solid rgba(255, 255, 255, 0.05); 58 | } 59 | 60 | .sidebar-text:hover { 61 | color: #16a085; 62 | transform: translateX(3px); 63 | } 64 | 65 | .sidebar-subtext { 66 | display: flex; 67 | justify-content: left; 68 | align-items: left; 69 | color: #9ca3af; 70 | font-size: calc(0.8em + 0.2vw); 71 | margin: -15px 0 -30px 15px; 72 | padding: 0.8rem; 73 | width: calc(100% - 1rem); 74 | white-space: nowrap; 75 | font-weight: 500; 76 | letter-spacing: 0.02em; 77 | text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.3); 78 | transition: all 0.3s ease; 79 | } 80 | 81 | .sidebar-subtext:hover { 82 | color: #16a085; 83 | transform: translateX(3px); 84 | } 85 | 86 | div[data-testid="stSidebarUserContent"] { 87 | height: calc(100vh - 60px); 88 | display: flex; 89 | flex-direction: column; 90 | padding: 1rem 0.5rem; 91 | } 92 | 93 | section[data-testid="stSidebar"] div.stButton { 94 | display: flex; 95 | position: fixed; 96 | bottom: 0; 97 | justify-content: flex-end; 98 | padding: 1rem; 99 | z-index: 1; 100 | background: linear-gradient(to top, rgba(32,34,34,0.95) 0%, rgba(32,34,34,0) 100%); 101 | } 102 | 103 | section[data-testid="stSidebar"] div.stButton > button { 104 | width: 110px; 105 | bottom: 15px; 106 | left: auto; 107 | border-radius: 8px; 108 | transition: all 0.2s ease; 109 | background: #2a2d2d; 110 | color: #e0e0e0; 111 | border: 1px solid rgba(255, 255, 255, 0.1); 112 | } 113 | 114 | section[data-testid="stSidebar"] div.stButton > button:hover { 115 | transform: translateY(-2px); 116 | background: #323535; 117 | border-color: rgba(255, 255, 255, 0.2); 118 | } 119 | 120 | .sidebar-footer { 121 | display: flex; 122 | position: fixed; 123 | bottom: 0; 124 | width: 100%; 125 | background-color: transparent; 126 | color: #94a3b8; 127 | z-index: 999; 128 | padding: 1rem; 129 | font-size: 0.9em; 130 | } 131 | 132 | .sidebar-divider { 133 | margin: 5px 0; 134 | border: 0; 135 | height: 1px; 136 | background: linear-gradient(to right, rgba(255,255,255,0.02) 0%, rgba(255,255,255,0.1) 50%, rgba(255,255,255,0.02) 100%); 137 | } 138 | 139 | .logo-container { 140 | display: flex; 141 | align-items: center; 142 | justify-content: flex-start; 143 | margin: 15px 0 0 0; 144 | padding: 0.5rem; 145 | background: transparent; 146 | border-radius: 12px; 147 | backdrop-filter: blur(10px); 148 | border: transparent; 149 | } 150 | 151 | .streamlit-expanderHeader { 152 | font-size: 1.2em; 153 | font-weight: 600; 154 | color: #e0e0e0; 155 | transition: color 0.2s ease; 156 | } 157 | 158 | .streamlit-expanderHeader:hover { 159 | color: #16a085; 160 | } 161 | 162 | .streamlit-expander { 163 | width: 100% !important; 164 | margin-left: 50px !important; 165 | min-width: 200px; 166 | max-width: 280px; 167 | border-radius: 8px; 168 | border: 1px solid rgba(255, 255, 255, 0.05); 169 | margin: 0.5rem 0; 170 | } 171 | 172 | .stSelectbox > div > div { 173 | background-color: rgba(42, 50, 49, 0.7) !important; 174 | } 175 | 176 | .stSelectbox [data-baseweb="select"] > div { 177 | margin-left: 10px !important; 178 | margin-top: -10px !important; 179 | min-width: 200px; 180 | max-width: 280px; 181 | border: 1px solid dark-grey !important; 182 | border-right: 5px solid #2f747a !important; 183 | } 184 | 185 | @media screen and (max-width: 768px) { 186 | [data-testid="stSidebar"][aria-expanded="true"] { 187 | min-width: 85vw; 188 | max-width: 85vw; 189 | } 190 | 191 | .sidebar-text { 192 | font-size: calc(1.2em + 0.5vw); 193 | } 194 | 195 | .logo-container { 196 | margin: 10px 0 0 0; 197 | margin-top: 100px; 198 | } 199 | } 200 | 201 | 202 | 203 | -------------------------------------------------------------------------------- /src/static/main_chat_input.css: -------------------------------------------------------------------------------- 1 | /* main_chat_input.css */ 2 | 3 | /* Chat input textarea hover/focus styles */ 4 | .stChatInput textarea, div[data-testid="stChatInput"] textarea { 5 | caret-color: #e0e0e0 !important; 6 | } 7 | 8 | 9 | /* Style the main chat input container */ 10 | .stChatInput, div[data-testid="stChatInput"] { 11 | height: auto !important; 12 | min-height: 150px !important; 13 | padding: 15px !important; 14 | border-radius: 36px !important; 15 | background-color: #292c2c !important; 16 | border: 1px solid rgba(255, 255, 255, 0.072) !important; 17 | box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2) !important; 18 | margin-top: -50px; 19 | } 20 | 21 | 22 | /* Style the inner container - new in 1.42.2 */ 23 | .st-emotion-cache-10nh6ap, div[data-testid="stChatInput"] > div { 24 | display: flex !important; 25 | flex-direction: column !important; 26 | width: 100% !important; 27 | border: none !important; 28 | box-shadow: none !important; 29 | background-color: transparent !important; 30 | width: 95% !important; 31 | max-width: 100% !important; 32 | height: auto !important; 33 | min-height: 150px !important; 34 | position: relative !important; 35 | } 36 | 37 | .st-emotion-cache-nbth8s { 38 | position: absolute !important; 39 | left: 10px !important; 40 | bottom: 20px !important; 41 | z-index: 10 !important; 42 | order: 2 !important; 43 | } 44 | 45 | /* Reposition the file upload button container */ 46 | [data-testid="stChatInputFileUploadButton"] { 47 | border-radius: 50% !important; 48 | padding-left: 9px !important; 49 | padding-bottom: 1px !important; 50 | align-self: right !important; 51 | /* margin-left: 679px !important; */ 52 | background-color: transparent !important; 53 | box-shadow: -0.5px 1px 0px rgba(0,0,0,0.1) !important; 54 | font-weight: 500 !important; 55 | font-size: 14px !important; 56 | cursor: pointer !important; 57 | transition: all 0.3s ease !important; 58 | width: 40px !important; 59 | height: 40px !important; 60 | left: -7px !important; 61 | bottom: -20px !important; 62 | position: absolute !important; 63 | z-index: 10 !important; 64 | } 65 | 66 | [data-testid="stChatInputFileUploadButton"] svg { 67 | fill: #8c9494 !important; 68 | } 69 | 70 | [data-testid="stChatInputFileUploadButton"]:hover { 71 | background-color: #c4d4d4 !important; 72 | color: #94bcbc !important; 73 | box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.3) !important; 74 | border-color: rgba(255, 255, 255, 0.2) !important; 75 | animation: pulse 0.6s ease-out !important; 76 | } 77 | 78 | [data-testid="stChatInputFileUploadButton"]:hover, [data-testid="stChatInputFileUploadButton"]:hover svg { 79 | fill: #212121 !important; 80 | } 81 | 82 | @keyframes pulse { 83 | 0% { transform: scale(1); } 84 | 50% { transform: scale(1.1); } 85 | 100% { transform: scale(1); } 86 | } 87 | 88 | /* Reposition line next 2 file upload button */ 89 | .st-emotion-cache-g39xc6 { 90 | 91 | margin-left: 29px !important; 92 | margin-right: 0px !important; 93 | border: none !important; 94 | position: absolute !important; 95 | bottom: 5px !important; 96 | background-color: transparent !important; 97 | z-index: 10 !important; 98 | order: 2 !important; 99 | min-height: 140px !important; 100 | } 101 | 102 | /* Style the textarea container */ 103 | .stChatInput [data-baseweb="textarea"] { 104 | border: none !important; 105 | box-shadow: none !important; 106 | background-color: transparent !important; 107 | width: 95% !important; 108 | max-width: 100% !important; 109 | order: 1 !important; 110 | } 111 | 112 | /* Style the actual textarea element */ 113 | [data-testid="stChatInputTextArea"] { 114 | min-height: 145px !important; 115 | max-height: 280px !important; 116 | width: 100% !important; 117 | max-width: 100% !important; 118 | overflow-y: auto !important; 119 | overflow-x: hidden !important; 120 | padding: 12px !important; 121 | padding-left: 35px !important; /* Make room for the attachment button */ 122 | font-size: 20px !important; 123 | line-height: 1.5 !important; 124 | box-shadow: none !important; 125 | word-wrap: break-word !important; 126 | white-space: pre-wrap !important; 127 | color: #e0e0e0 !important; 128 | background-color: transparent !important; 129 | } 130 | 131 | [data-testid="stChatInputTextArea"]::placeholder { 132 | color: rgba(224, 225, 225, 0.856) !important; 133 | } 134 | 135 | /* Style the base input container */ 136 | [data-baseweb="base-input"] { 137 | border: none !important; 138 | width: 100% !important; 139 | max-width: 96% !important; 140 | box-shadow: none !important; 141 | background-color: transparent !important; 142 | min-height: 110px !important; 143 | max-height: 280px !important; 144 | } 145 | 146 | /* Style the button container - new in 1.42.2 */ 147 | .st-emotion-cache-sey4o0 { 148 | align-self: flex-end !important; 149 | z-index: 5 !important; 150 | 151 | } 152 | 153 | /* Adjust the send button position */ 154 | [data-testid="stChatInputSubmitButton"] { 155 | border-radius: 50% !important; 156 | align-self: flex-end !important; 157 | margin-left: 10px !important; 158 | color: #e0e0e0 !important; 159 | background-color: #2f747a !important; 160 | margin-bottom: 1px !important; 161 | box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2) !important; 162 | border: 1px solid rgba(255, 255, 255, 0.1) !important; 163 | font-weight: 500 !important; 164 | font-size: 14px !important; 165 | cursor: pointer !important; 166 | transition: all 0.3s ease !important; 167 | } 168 | 169 | [data-testid="stChatInputSubmitButton"]:hover { 170 | background-color: #68abac !important; 171 | color: #212121 !important; 172 | box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.3) !important; 173 | border-color: rgba(255, 255, 255, 0.2) !important; 174 | animation: pulse 0.6s ease-out !important; 175 | } 176 | 177 | @keyframes pulse { 178 | 0% { transform: scale(1); } 179 | 50% { transform: scale(1.1); } 180 | 100% { transform: scale(1); } 181 | } -------------------------------------------------------------------------------- /src/static/secondpage.css: -------------------------------------------------------------------------------- 1 | /* Custom styling for inline code */ 2 | [data-testid="stChatMessage"] .assistant-title-start code { 3 | background-color: #f0f0f0; 4 | vertical-align: middle; 5 | font-family: monospace; 6 | font-size: 0.65em; 7 | color: #6f8695; 8 | font-weight: 500; 9 | } 10 | [data-testid="stChatMessage"] .assistant-title code { 11 | background-color: #f0f0f0; 12 | vertical-align: middle; 13 | font-family: monospace; 14 | font-size: 0.65em; 15 | color: #6f8695; 16 | font-weight: 500; 17 | } 18 | [data-testid="stChatMessage"] .assistant-title-start { 19 | color: #CC7900; 20 | margin-top: -10px; 21 | } 22 | [data-testid="stChatMessage"] .cyan-title-start { 23 | color: #22808e; 24 | margin-top: -10px; 25 | } 26 | [data-testid="stChatMessage"] .cyan-title-start code { 27 | color: #e83e8c; 28 | margin-top: -10px; 29 | } 30 | [data-testid="stChatMessage"] .assistant-title { 31 | color: #CC7900; 32 | margin-top: 0px; 33 | } 34 | [data-testid="stChatMessage"] .human-heading { 35 | color: #FF9800; 36 | } 37 | [data-testid="stChatMessage"] .ai-heading { 38 | color: #2196F3; 39 | } 40 | [data-testid="stChatMessage"] .tool-heading { 41 | color: #FF9800; 42 | } 43 | [data-testid="stChatMessage"] .expander-title { 44 | color: #673AB7; 45 | } 46 | [data-testid="stChatMessage"] .stop-button { 47 | background-color: #ff4b4b; 48 | color: white; 49 | border: none; 50 | border-radius: 50%; 51 | width: 40px; 52 | height: 40px; 53 | font-size: 20px; 54 | cursor: pointer; 55 | display: flex; 56 | align-items: center; 57 | justify-content: center; 58 | transition: background-color 0.3s; 59 | } 60 | .stop-button:hover { 61 | background-color: #ff0000; 62 | } 63 | 64 | 65 | [data-testid="stChatMessage"] { 66 | max-width: 100%; /* Use a percentage of the viewport width */ 67 | width: auto; /* Let the width be determined by content */ 68 | margin-left: 4vw; 69 | margin-right: 5vw; 70 | overflow-wrap: break-word; /* Break long words to prevent overflow */ 71 | word-wrap: break-word; /* Legacy support for smaller browsers */ 72 | overflow: hidden; /* Hide overflowed text */ 73 | } 74 | 75 | .block-container { 76 | padding-top: 1.5rem; 77 | padding-bottom: 0rem; 78 | padding-left: 0.5rem; 79 | padding-right: 1.5em; 80 | } 81 | .main .block-container { 82 | max-width: 100%; 83 | width: 100%; 84 | padding: 0rem 0rem; 85 | } 86 | .stApp { 87 | margin: 0 auto; 88 | } 89 | 90 | /* For user messages 91 | .st-emotion-cache-janbn0 { 92 | flex-direction: row-reverse; 93 | text-align: right; 94 | }*/ 95 | .stChatInput textarea, div[data-testid="stChatInput"] textarea { 96 | caret-color: #e0e0e0 !important; 97 | } 98 | 99 | [data-testid="stButton"] button { 100 | position: fixed; 101 | bottom: 55px; 102 | font-size: 35px; 103 | height: 38px; 104 | width: 38px; 105 | margin-right: -62px; 106 | display: flex; 107 | align-items: center; 108 | justify-content: center; 109 | transition: all 5s ease-out; /* Smooth transitions */ 110 | background-color: #2a2c2c; 111 | z-index: 999; /* Ensure button stays on top */ 112 | } 113 | 114 | [data-testid="stButton"] button:hover { 115 | position: fixed; 116 | transition: all 5s ease-out; /* Smooth transitions */ 117 | left: 10px; 118 | bottom: 30px; 119 | background-color: #2a2c2c; 120 | } 121 | 122 | .stChatInput, div[data-testid="stChatInput"] { 123 | height: auto !important; 124 | min-height: 40px !important; 125 | padding: 15px !important; 126 | border-radius: 12px !important; 127 | background-color: #2a2c2c !important; 128 | box-shadow: 0 1px 5px rgba(1,0,0,0.3) !important; 129 | margin-top: -20px; 130 | margin-bottom: -25px; 131 | } 132 | 133 | [data-testid="stChatInputTextArea"]::placeholder { 134 | color: rgba(224, 225, 225, 0.856) !important; 135 | } 136 | 137 | [data-testid="stChatInputFileUploadButton"] svg { 138 | fill: #8c9494 !important; 139 | } 140 | 141 | /* Style the inner container - new in 1.42.2 */ 142 | .st-emotion-cache-10nh6ap, div[data-testid="stChatInput"] > div { 143 | display: flex !important; 144 | flex-direction: column !important; 145 | width: 100% !important; 146 | border: none !important; 147 | box-shadow: none !important; 148 | background-color: transparent !important; 149 | width: 95% !important; 150 | max-width: 100% !important; 151 | height: auto !important; 152 | min-height: 40px !important; 153 | position: relative !important; 154 | } 155 | 156 | .st-emotion-cache-nbth8s { 157 | position: absolute !important; 158 | left: 10px !important; 159 | bottom: 20px !important; 160 | z-index: 10 !important; 161 | order: 2 !important; 162 | } 163 | 164 | /* Reposition the file upload button container */ 165 | [data-testid="stChatInputFileUploadButton"] { 166 | margin-right: 10px !important; 167 | border: none !important; 168 | position: absolute !important; 169 | bottom: -16px !important; 170 | z-index: 10 !important; 171 | order: 2 !important; 172 | } 173 | 174 | /* Reposition line next 2 file upload button */ 175 | .st-emotion-cache-g39xc6 { 176 | margin-left: 29px !important; 177 | margin-right: 0px !important; 178 | border: none !important; 179 | position: absolute !important; 180 | bottom: -20px !important; 181 | background-color: transparent !important; 182 | z-index: 10 !important; 183 | order: 2 !important; 184 | min-height: 40px !important; 185 | } 186 | 187 | /* Style the textarea container */ 188 | .stChatInput [data-baseweb="textarea"] { 189 | border: none !important; 190 | box-shadow: none !important; 191 | background-color: transparent !important; 192 | width: 95% !important; 193 | max-width: 100% !important; 194 | order: 1 !important; 195 | } 196 | 197 | /* Style the actual textarea element */ 198 | [data-testid="stChatInputTextArea"] { 199 | min-height: 40px !important; 200 | max-height: 100px !important; 201 | width: 100% !important; 202 | max-width: 100% !important; 203 | overflow-y: auto !important; 204 | overflow-x: hidden !important; 205 | padding: 12px !important; 206 | padding-left: 35px !important; /* Make room for the attachment button */ 207 | font-size: 16px !important; 208 | line-height: 1.5 !important; 209 | box-shadow: none !important; 210 | word-wrap: break-word !important; 211 | white-space: pre-wrap !important; 212 | } 213 | 214 | /* Style the base input container */ 215 | [data-baseweb="base-input"] { 216 | border: none !important; 217 | width: 100% !important; 218 | max-width: 96% !important; 219 | box-shadow: none !important; 220 | background-color: transparent !important; 221 | min-height: 10px !important; 222 | max-height: 100px !important; 223 | } 224 | 225 | /* Style the button container - new in 1.42.2 */ 226 | .st-emotion-cache-sey4o0 { 227 | align-self: flex-end !important; 228 | margin-top: -30px !important; 229 | z-index: 5 !important; 230 | } 231 | 232 | /* Adjust the send button position */ 233 | [data-testid="stChatInputSubmitButton"] { 234 | align-self: flex-end !important; 235 | margin-bottom: -27px !important; 236 | box-shadow: 0 1px 0px rgba(0,0,0,0.1) !important; 237 | } -------------------------------------------------------------------------------- /src/static/mian_area.css: -------------------------------------------------------------------------------- 1 | /* main_area.css */ 2 | 3 | /* Global font and styling improvements */ 4 | body { 5 | font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif !important; 6 | -webkit-font-smoothing: antialiased; 7 | -moz-osx-font-smoothing: grayscale; 8 | color: #1A1A1A; 9 | } 10 | 11 | /* Main container styling */ 12 | .main .block-container { 13 | max-width: 1200px; 14 | margin: 0 auto; 15 | padding: 1rem 2rem; 16 | height: 100vh; 17 | overflow-y: auto; 18 | } 19 | 20 | /* Perplexity-inspired clean chat container */ 21 | .chat-container { 22 | display: flex; 23 | flex-direction: column; 24 | background-color: transparent; 25 | border-radius: 10px; 26 | box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05); 27 | margin-top: 0px; 28 | overflow: hidden; 29 | } 30 | 31 | /* Message styling with Perplexity-inspired clean look */ 32 | .message { 33 | padding: 1.25rem; 34 | margin: 0.75rem 0; 35 | border-radius: 12px; 36 | width: 100%; 37 | line-height: 1.6; 38 | position: relative; 39 | animation: fadeIn 0.3s ease-in-out; 40 | background-color: #2a2d2d; 41 | border: 1px solid rgba(255, 255, 255, 0.1); 42 | } 43 | 44 | @keyframes fadeIn { 45 | from { opacity: 0; transform: translateY(10px); } 46 | to { opacity: 1; transform: translateY(0); } 47 | } 48 | 49 | .user-message { 50 | background-color: #292c2c; 51 | color: #e0e0e0; 52 | } 53 | 54 | .ai-message { 55 | background-color: #2a2d2d; 56 | color: #e0e0e0; 57 | border: 1px solid rgba(255, 255, 255, 0.1); 58 | box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2); 59 | } 60 | 61 | /* Perplexity-inspired empty chat state */ 62 | .empty-chat-container { 63 | display: flex; 64 | flex-direction: column; 65 | justify-content: center; 66 | align-items: center; 67 | padding: 1rem 1rem; 68 | text-align: center; 69 | margin-top: -4em; 70 | animation: fadeInUp 0.6s ease-out; 71 | } 72 | 73 | @keyframes fadeInUp { 74 | from { opacity: 0; transform: translateY(20px); } 75 | to { opacity: 1; transform: translateY(0); } 76 | } 77 | 78 | .welcome-text { 79 | font-size: 1.8rem; 80 | font-weight: 600; 81 | color: #9ca1af !important; 82 | margin-top: -2.5em; 83 | margin-bottom: 1.5rem; 84 | } 85 | 86 | .welcome-subtext { 87 | font-size: 1.1rem; 88 | color: #8c9494 !important; 89 | max-width: 600px; 90 | margin-bottom: 3rem; 91 | } 92 | 93 | /* Custom styling for inline code */ 94 | .welcome-subtext code { 95 | background-color: #323535; 96 | padding: 2px 5px; 97 | border-radius: 4px; 98 | font-family: monospace; 99 | font-size: 0.9em; 100 | color: #e83e8c; 101 | font-weight: 500; 102 | } 103 | 104 | /* Enhanced chat input container */ 105 | .chat-input-container { 106 | width: 80%; 107 | max-width: 800px; 108 | margin: 0 auto; 109 | transition: all 0.3s ease; 110 | } 111 | 112 | /* Compact feature cards with pastel colors */ 113 | .feature-cards-container { 114 | display: flex; 115 | justify-content: center; 116 | gap: 16px; 117 | margin: 1rem 0; 118 | flex-wrap: wrap; 119 | } 120 | 121 | .feature-card { 122 | background: #2a2d2d; 123 | border-radius: 12px; 124 | padding: 1rem; 125 | width: 220px; 126 | box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2); 127 | text-align: center; 128 | transition: all 0.3s ease; 129 | border: 1px solid rgba(255, 255, 255, 0.1); 130 | position: relative; 131 | overflow: hidden; 132 | } 133 | 134 | /* Enhanced background colors for feature cards */ 135 | .feature-card:nth-child(1) { 136 | background: #2a2d2d; 137 | border-bottom: 4px solid #2f747a; 138 | margin-bottom: 1rem; 139 | } 140 | 141 | .feature-card:nth-child(2) { 142 | background: #2a2d2d; 143 | border-bottom: 4px solid #2f747a; 144 | margin-bottom: 1rem; 145 | } 146 | 147 | .feature-card:nth-child(3) { 148 | background: #2a2d2d; 149 | border-bottom: 4px solid #2f747a; 150 | margin-bottom: 1rem; 151 | } 152 | 153 | .feature-card:hover { 154 | transform: translateY(-6px) scale(1.02); 155 | box-shadow: 0 10px 25px rgba(0, 0, 0, 0.06); 156 | } 157 | 158 | /* Add subtle card decoration */ 159 | .feature-card::after { 160 | content: ''; 161 | position: absolute; 162 | top: 0; 163 | right: 0; 164 | width: 60px; 165 | height: 60px; 166 | background: rgba(255, 255, 255, 0.1); 167 | border-radius: 0 0 0 60px; 168 | opacity: 0; 169 | transition: all 0.3s ease; 170 | } 171 | 172 | .feature-card:hover::after { 173 | opacity: 1; 174 | } 175 | 176 | .feature-icon { 177 | font-size: 1.8rem; 178 | margin-bottom: 0.8rem; 179 | background: #191a1a; 180 | width: 50px; 181 | height: 50px; 182 | display: flex; 183 | align-items: center; 184 | justify-content: center; 185 | border-radius: 50%; 186 | margin-left: auto; 187 | margin-right: auto; 188 | box-shadow: 0 3px 8px rgba(0, 0, 0, 0.06); 189 | } 190 | 191 | .feature-title { 192 | font-weight: 600; 193 | font-size: 1.1rem; 194 | margin-bottom: 0.75rem; 195 | color: #e0e0e0ea; 196 | } 197 | 198 | .feature-description { 199 | font-size: 0.9rem; 200 | color: #9ca3af; 201 | line-height: 1.5; 202 | } 203 | 204 | /* Enhanced example section */ 205 | .examples-container { 206 | background-color: #2a2d2d; 207 | border-radius: 12px; 208 | padding: 1.2rem; 209 | margin: 1rem auto; 210 | max-width: 800px; 211 | border: 1px solid rgba(255, 255, 255, 0.1); 212 | box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2); 213 | } 214 | 215 | /* Perplexity-inspired heading styles */ 216 | .section-heading { 217 | font-size: 1.25rem; 218 | font-weight: 600; 219 | color: #1A1A1A; 220 | margin-bottom: 1rem; 221 | letter-spacing: -0.01em; 222 | } 223 | 224 | /* Responsive adjustments */ 225 | @media screen and (max-width: 992px) { 226 | .chat-input-container, .empty-chat-container, .chat-container, .examples-container { 227 | max-width: 95%; 228 | } 229 | } 230 | 231 | @media screen and (max-width: 768px) { 232 | .welcome-text { 233 | font-size: 1.5rem; 234 | } 235 | 236 | .feature-cards-container { 237 | flex-direction: column; 238 | align-items: center; 239 | } 240 | 241 | .feature-card { 242 | width: 100%; 243 | max-width: 280px; 244 | } 245 | } 246 | 247 | /* Perplexity-inspired footer */ 248 | .app-footer { 249 | display: flex; 250 | justify-content: center; 251 | padding: 1rem 0; 252 | margin-top: 0.75rem; 253 | color: #ad9b8d; 254 | font-size: 0.875rem; 255 | } 256 | 257 | .footer-link { 258 | color: #ad9b8d; 259 | margin: 0 0.5rem; 260 | text-decoration: none; 261 | transition: color 0.2s ease; 262 | } 263 | 264 | .footer-link:hover { 265 | color: #22808e; 266 | text-decoration: underline; 267 | } 268 | 269 | /* WELCOME TEXT */ 270 | 271 | .welcome-about { 272 | font-size: 1rem; 273 | color: #9ca3af; 274 | margin-top: -1.5em; 275 | margin-bottom: -5em; 276 | } 277 | 278 | .logo-container-main { 279 | display: flex; 280 | align-items: center; 281 | justify-content: center; 282 | margin: 5px auto -10px auto; 283 | } 284 | 285 | .image-main { 286 | width: 96%; /* Reduced size to fit inline */ 287 | height: 96%; /* Reduced size to fit inline */ 288 | transform-style: preserve-3d; 289 | z-index: 9999; 290 | overflow: hidden; 291 | display: inline-block; 292 | vertical-align: middle; 293 | margin-top: 2%; 294 | margin-bottom: -5%; 295 | margin-right: -10px; 296 | } 297 | 298 | /* This rule targets the sidebar when its aria-expanded attribute is false */ 299 | [data-testid="stSidebar"][aria-expanded="false"] .note-ai { 300 | display: none; 301 | } -------------------------------------------------------------------------------- /src/app.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from langchain_core.messages import ( 3 | AIMessage, 4 | BaseMessage, 5 | HumanMessage, 6 | ToolMessage, 7 | ) 8 | from langchain_core.prompts import ChatPromptTemplate 9 | from langchain_core.runnables import RunnableLambda, RunnableWithFallbacks 10 | from langchain_core.tools import tool 11 | from langchain_community.agent_toolkits import SQLDatabaseToolkit 12 | from langchain_community.document_loaders import PyPDFLoader 13 | from langchain_community.embeddings import HuggingFaceEmbeddings 14 | from langchain_community.utilities import SQLDatabase 15 | from langchain_community.vectorstores import FAISS 16 | from langgraph.graph import END, StateGraph, START 17 | from langgraph.graph.message import AnyMessage, add_messages 18 | from langgraph.prebuilt import ToolNode 19 | from config.config_loader import ConfigLoader # CUSTOM CLASS 20 | from models.model_loader import ModelLoader # CUSTOM CLASS 21 | from utils.constants import TASK_OPTIONS #, DEFAULT_MODEL_PARAMS # CUSTOM CLASS/PARAMS 22 | from utils.paths import PathManager # CUSTOM CLASS 23 | from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler 24 | from IPython.display import Image, display 25 | from langchain.text_splitter import RecursiveCharacterTextSplitter 26 | from langchain.chains import ConversationalRetrievalChain 27 | from langchain_core.pydantic_v1 import BaseModel, Field 28 | from langchain_core.runnables import RunnablePassthrough 29 | # from ollama_functions_custom import OllamaFunctions 30 | from pydantic import BaseModel 31 | from sentence_transformers import SentenceTransformer 32 | from tabulate import tabulate 33 | from typing import ( 34 | Annotated, 35 | Dict, 36 | List, 37 | Literal, 38 | Optional, 39 | TypedDict, 40 | ) 41 | import base64 42 | import contextlib 43 | import faiss 44 | import gc 45 | import getpass 46 | import io 47 | import json 48 | import matplotlib.pyplot as plt 49 | import numpy as np 50 | import os 51 | import pandas as pd 52 | import pickle 53 | import plotly.express as px 54 | import plotly.graph_objects as go 55 | import plotly.io as pio 56 | import polars as pl 57 | import pyarrow.feather as feather 58 | import pyarrow.parquet as pq 59 | import re 60 | import sys 61 | import tempfile 62 | import textwrap 63 | import threading 64 | import time 65 | import torch 66 | import traceback 67 | import yaml 68 | # from sql_utility import list_tables_tool 69 | # from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool, InfoSQLDatabaseTool, ListSQLDatabaseTool, QuerySQLCheckerTool 70 | # from tqdm.autonotebook import tqdm, trange 71 | # from langchain_groq import ChatGroq 72 | # from langchain_ollama import ChatOllama 73 | # from langchain_community.chat_models import ChatLlamaCpp 74 | # from langchain_core.runnables import RunnablePassthrough 75 | 76 | # Define global variables at the module level 77 | # os.environ["POLARS_MAX_THREADS"] = "1" 78 | 79 | # PAUSE 80 | Pause_productivity_Analysis = True 81 | model_embeddings, llm_agent = None, None 82 | 83 | # LLMs 84 | 85 | if 'second_task' not in st.session_state: 86 | st.session_state.second_task = False 87 | 88 | 89 | @st.cache_resource(show_spinner="Loading models...") 90 | def load_models(): 91 | """ 92 | Load models 93 | """ 94 | # --- Progress Indicators --- 95 | progress_bar = st.progress(0) 96 | progress_text = st.empty() 97 | 98 | # --- Load LLMs --- 99 | progress_text.text("Loading LLMs...") 100 | print("Loading LLMs...") # Debug print 101 | 102 | gc.collect() 103 | torch.cuda.empty_cache() 104 | 105 | progress_bar.progress(25) 106 | 107 | print('---') 108 | progress_bar.progress(50) 109 | 110 | print('---') 111 | progress_bar.progress(100) 112 | 113 | # --- Cleanup --- 114 | progress_text.empty() 115 | progress_bar.empty() 116 | 117 | print("--- Finished load_models ---") # Debug print 118 | 119 | model_embeddings, llm_agent = None, None 120 | return model_embeddings, llm_agent 121 | 122 | def initialize_session_state(): 123 | if 'stop_streaming' not in st.session_state: 124 | st.session_state.stop_streaming = False 125 | if "messages" not in st.session_state: 126 | st.session_state.messages = [] 127 | if "statistics" not in st.session_state: 128 | st.session_state.statistics = [] 129 | if "context" not in st.session_state: 130 | st.session_state.context = "" 131 | 132 | def setup_sidebar(): 133 | with open("./assets/logo.png", "rb") as image_file: 134 | base64_image = base64.b64encode(image_file.read()).decode("utf-8") 135 | 136 | with open("./src/static/about.css") as f: 137 | st.markdown(f"", unsafe_allow_html=True) 138 | 139 | with open("./src/static/sidebar.css") as f: 140 | st.sidebar.markdown(f"", unsafe_allow_html=True) 141 | 142 | logo_and_text = f""" 143 |
144 | 145 |
Synapse
146 |
147 | """ 148 | st.sidebar.markdown(logo_and_text, unsafe_allow_html=True) 149 | st.sidebar.markdown("
Explore AI Agents Workflows
", unsafe_allow_html=True) 150 | st.sidebar.markdown("
", unsafe_allow_html=True) 151 | 152 | return st.sidebar.selectbox(options = TASK_OPTIONS, label='', label_visibility="collapsed") 153 | 154 | def set_about_section(): 155 | with st.sidebar.expander("About"): 156 | with open("./src/static/about.css") as f: 157 | st.markdown(f"", unsafe_allow_html=True) 158 | with open("./src/static/about.html") as f: 159 | st.markdown(f"{f.read()}", unsafe_allow_html=True) 160 | 161 | with open("./src/static/note_ai.css") as f: 162 | st.markdown(f"", unsafe_allow_html=True) 163 | st.sidebar.markdown("""

Occasionally, AI workflows may produce unexpected results.

""", unsafe_allow_html=True) 164 | 165 | def main(): 166 | try: 167 | st.set_page_config( 168 | page_title="productivity Chatbot | Acrivon Phosphoproteomics", 169 | page_icon="./assets/logo.png", 170 | layout="wide", 171 | initial_sidebar_state="expanded" 172 | ) 173 | except: 174 | pass 175 | 176 | if 'inner_auto' not in st.session_state: 177 | st.session_state.inner_auto = False 178 | 179 | print("--- Starting main function ---") 180 | load_success = False 181 | try: 182 | print("Calling load_models_dataframe...") 183 | model_embeddings, llm_agent = load_models() # Ensure globals are set 184 | load_success = True 185 | except Exception as e: 186 | st.error(f"Fatal error during resource loading: {e}") 187 | st.exception(e) # Show full traceback in Streamlit for detailed debugging 188 | print(f"Exception during load_models_dataframe call: {e}") # Debug 189 | st.stop() # Stop the Streamlit app execution 190 | 191 | with open("./app/style.css") as css: 192 | st.markdown(f'', unsafe_allow_html=True) 193 | 194 | # Read and encode the logo image 195 | with open("./assets/logo.png", "rb") as f: 196 | logo_base64 = base64.b64encode(f.read()).decode() 197 | 198 | path_manager = PathManager() 199 | 200 | try: 201 | initialize_session_state() 202 | selected_task = setup_sidebar() 203 | 204 | with open("./src/static/mian_area.css") as f: 205 | st.markdown(f"", unsafe_allow_html=True) 206 | set_about_section() 207 | 208 | if 'first_question' not in st.session_state or len(st.session_state.first_question) == 0: 209 | with open("./src/static/main_chat_input.css") as f: 210 | st.markdown(f"", unsafe_allow_html=True) 211 | col1, col2, col3 = st.columns([1, 16, 1]) 212 | files_exist = False 213 | with col2: 214 | st.markdown(""" 215 |
216 |

Welcome to Synapse Workflows

217 |

218 | Interact with powerful AI Agent Workflows using natural language. Try running
219 | Smart Search Agent, Productivity Assistant, or Data Analysis Agent.
220 | Ready to explore the world of intelligent agents? 221 |


222 |
223 | """, unsafe_allow_html=True) 224 | 225 | if selected_task == "Smart Search Agent": 226 | total_input = st.chat_input( 227 | key="initial_query", 228 | placeholder="Search or Ask anything...", 229 | ) 230 | 231 | user_input = total_input 232 | elif selected_task == "Auto Selection": 233 | total_input = st.chat_input( 234 | key="initial_query", 235 | accept_file = "multiple", 236 | file_type = ["pdf", "jpg", "jpeg", "png", "doc", "docx"], 237 | placeholder = ( 238 | "Ask anything...\n\nNot sure which agent you need? " 239 | "Type your question or upload files, and Synapse will auto-select the best workflow for you!" 240 | ), 241 | ) 242 | 243 | if total_input: 244 | if hasattr(total_input, 'text') and total_input.text: 245 | user_input = total_input.text 246 | if hasattr(total_input, 'files') and total_input.files: 247 | files_collected = total_input['files'] 248 | files_exist = True 249 | else: 250 | total_input = st.chat_input( 251 | key="initial_query", 252 | accept_file = "multiple", 253 | file_type = ["pdf", "jpg", "jpeg", "png", "doc", "docx"], 254 | placeholder="Ask Anything...", 255 | ) 256 | 257 | if total_input: 258 | if hasattr(total_input, 'text') and total_input.text: 259 | user_input = total_input.text 260 | if hasattr(total_input, 'files') and total_input.files: 261 | files_collected = total_input['files'] 262 | files_exist = True 263 | 264 | if total_input: 265 | if 'first_question' not in st.session_state: 266 | st.session_state.first_question = [] 267 | 268 | if not files_exist: 269 | st.session_state.first_question.append({"role": "initializer", "content": user_input, "file": None}) 270 | else: 271 | st.session_state.first_question.append({"role": "initializer", "content": user_input, "file": files_collected}) 272 | 273 | st.rerun() 274 | 275 | # Load custom HTML layout 276 | with open("./src/static/static_feature_card.html", "r", encoding="utf-8") as f: 277 | html_content = f.read() 278 | st.markdown(html_content, unsafe_allow_html=True) 279 | 280 | from streamlit_extras.bottom_container import bottom 281 | 282 | with bottom(): 283 | with open("./src/static/links.html", "r", encoding="utf-8") as f: 284 | html_content = f.read() 285 | st.markdown( 286 | f""" 287 |
288 | {html_content} 289 |
290 | """, 291 | unsafe_allow_html=True 292 | ) 293 | 294 | else: 295 | task = selected_task 296 | if selected_task=='Auto Selection' and 'global_task' not in st.session_state: 297 | st.session_state.global_task = "Auto Selection" 298 | elif 'global_task' in st.session_state and st.session_state.global_task == "Inner Auto Selection": 299 | pass 300 | 301 | if 'stop_streaming' not in st.session_state: 302 | st.session_state.stop_streaming = False 303 | 304 | if "messages" not in st.session_state: 305 | st.session_state.messages = [] 306 | st.session_state.context = "" 307 | 308 | from PIL import Image 309 | def process_avatar(image_path, output_size=200): 310 | try: 311 | img = Image.open(image_path) 312 | if img.mode != "RGBA": 313 | img = img.convert("RGBA") 314 | 315 | # Create a square canvas with transparency 316 | canvas = Image.new("RGBA", (output_size, output_size), (0, 0, 0, 0)) 317 | 318 | # Maintain aspect ratio 319 | width, height = img.size 320 | ratio = min(output_size / width, output_size / height) 321 | new_size = (int(width * ratio), int(height * ratio)) 322 | 323 | # Resize the image while maintaining aspect ratio 324 | resized = img.resize(new_size, Image.LANCZOS) 325 | 326 | # Center the resized image on the canvas 327 | x = (output_size - new_size[0]) // 2 328 | y = (output_size - new_size[1]) // 2 329 | canvas.paste(resized, (x, y), resized) # Use the resized image as a mask for transparency 330 | 331 | processed_path = os.path.join("./assets", "processed_" + os.path.basename(image_path)) 332 | os.makedirs(os.path.dirname(processed_path), exist_ok=True) 333 | canvas.save(processed_path) 334 | 335 | return processed_path 336 | 337 | except FileNotFoundError: 338 | print(f"Avatar image not found at: {image_path}") 339 | return None 340 | except Exception as e: 341 | print(f"An error occurred: {e}") 342 | 343 | assistant_avatar = process_avatar("./assets/logo.png") 344 | user_avatar = "./assets/logo.png" 345 | 346 | # color_scheme = { 347 | # 'assistant-title': '#CC7900', 348 | # 'human_heading': '#FF9800', # Green for Human Message 349 | # 'ai_heading': '#2196F3', # Blue for AI Message 350 | # 'tool_heading': '#FF9800', # Orange for Tool Message 351 | # 'expander_title': '#673AB7', # Deep Purple for Expander 352 | # 'cyan-title-start': '#22808e', # Use magenta 353 | # 'cyan-title-start-code': '#e83e8c', # 354 | # } 355 | # CSS to style the headings .stop-button {{color: 'tool_heading']}; cursor: pointer; margin-left: 10px;}} 356 | 357 | with open("./src/static/secondpage.css") as f: 358 | st.markdown(f"", unsafe_allow_html=True) 359 | 360 | st.markdown(""" 361 | 440 | """, unsafe_allow_html=True) 441 | 442 | 443 | # Process uploaded PDFs 444 | def process_pdf(pdf_list, chunk_size=1000, chunk_overlap=100): 445 | total_chunks = 0 446 | page_content_lengths = [] 447 | with st.spinner("📚 Processing your PDFs... Please wait while we extract all that knowledge!"): 448 | for file in pdf_list: 449 | with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp: 450 | tmp.write(file.getvalue()) 451 | time.sleep(1) 452 | tmp_path = tmp.name 453 | 454 | loader = PyPDFLoader(tmp_path) 455 | documents = loader.load() 456 | 457 | os.unlink(tmp_path) 458 | 459 | text_splitter = RecursiveCharacterTextSplitter( 460 | chunk_size=1000, 461 | chunk_overlap=200 462 | ) 463 | chunks = text_splitter.split_documents(documents) 464 | page_content_lengths.extend([len(chunk.page_content) for chunk in chunks]) 465 | 466 | embeddings = model_pdf #HuggingFaceEmbeddings(model_name=str(path_manager.model_paths["EMBEDDINGS_qwen"])) 467 | # print(">>>>", str(path_manager.model_paths["EMBEDDINGS_qwen"])) 468 | 469 | # Create or update vector store 470 | if st.session_state.vectorstore: 471 | st.session_state.vectorstore.add_documents(chunks) 472 | else: 473 | st.session_state.vectorstore = FAISS.from_documents(chunks, embeddings) 474 | 475 | total_chunks += len(chunks) 476 | 477 | fun_facts = [ 478 | "Fun fact: The average business document contains only 20% truly unique content.", 479 | "AI Tidbit: These embeddings help your AI understand meaning, not just words!", 480 | "Tech note: Vector databases are how AIs can 'remember' your documents.", 481 | "Curious? Each 'chunk' is like a small puzzle piece of your document's knowledge!" 482 | ] 483 | import random 484 | 485 | st.session_state.pdf_processed = { 486 | "num_chunks": total_chunks, 487 | "fun_fact": random.choice(fun_facts), 488 | "num_documents": len(pdf_list), 489 | "chunk_sizes": page_content_lengths if total_chunks > 0 else [] 490 | } 491 | 492 | return total_chunks 493 | 494 | def supervisor_node(user_input: str): 495 | class Router(BaseModel): 496 | """Worker to route to next.""" 497 | next: Literal["smart_search", "productivity_assistant", "data_analysis_agent"] 498 | 499 | members = ["smart_search", "productivity_assistant", "data_analysis_agent"] 500 | 501 | system_prompt = f"""You are a supervisor tasked with managing a conversation between the following workers: {members}. 502 | Given the user request, determine which worker should handle it next. 503 | 504 | Return ONLY a JSON object with a single field 'next' that must be one of: "smart_search" or "productivity_assistant" or "data_analysis_agent". 505 | 506 | Example response format: 507 | {{{{ 508 | "next": "productivity_assistant" 509 | }}}} 510 | 511 | Routing criteria with detailed examples: 512 | - Route to "smart_search" when the query involves finding information, answering factual questions, or researching topics: 513 | * "What is the latest research on large language models?" 514 | * "Find information about climate change impacts" 515 | * "Who invented the internet?" 516 | * "What are the symptoms of COVID-19?" 517 | * "Search for recent advancements in renewable energy" 518 | * "Find tutorials on Python programming" 519 | * "What happened in world news today?" 520 | * "Look up the definition of quantum computing" 521 | 522 | - Route to "productivity_assistant" when the query involves personal organization, scheduling, reminders, or task management: 523 | * "Create a to-do list for my project" 524 | * "Schedule a meeting for tomorrow at 2pm" 525 | * "Remind me to call John in 3 hours" 526 | * "Draft an email to my team about the upcoming deadline" 527 | * "Help me organize my work schedule" 528 | * "Create a shopping list for dinner ingredients" 529 | * "Set up a daily reminder for medication" 530 | * "Plan my study schedule for next week" 531 | 532 | - Route to "data_analysis_agent" when the query involves analyzing, visualizing, or processing data: 533 | * "Analyze this CSV file of sales data" 534 | * "Create a chart showing revenue trends" 535 | * "Calculate the mean and standard deviation of these numbers" 536 | * "Compare these two datasets and find correlations" 537 | * "Visualize this data as a histogram" 538 | * "Perform sentiment analysis on these customer reviews" 539 | * "Extract insights from this survey data" 540 | * "Identify patterns in this time series data" 541 | * "Generate a report based on these quarterly figures" 542 | 543 | When in doubt about which agent to route to, consider: 544 | - If the query is primarily about finding or retrieving information, use "smart_search" 545 | - If the query is about personal task management or organization, use "productivity_assistant" 546 | - If the query involves working with or making sense of data, use "data_analysis_agent" 547 | """ 548 | 549 | from langchain_core.output_parsers import JsonOutputParser 550 | from langchain_core.prompts import PromptTemplate 551 | 552 | parser = JsonOutputParser(pydantic_object=Router) 553 | prompt = PromptTemplate( 554 | template="Answer the user query.\n{system_prompt}\n{format_instructions}\nUser: {input}\n", 555 | input_variables=["system_prompt","input"], 556 | partial_variables={"format_instructions": parser.get_format_instructions()}, 557 | ) 558 | 559 | try: 560 | router_chain = prompt | llm_router | parser 561 | response = router_chain.invoke({"system_prompt":system_prompt, "input": user_input}) 562 | print("Structured response:", response) 563 | return response.get('next', 'smart_search') # Default to smart_search instead of literature 564 | except Exception as e: 565 | print(f"Structured output parsing error: {e}") 566 | # Default to smart_search instead of literature if there's an error 567 | return Router(next="smart_search").next 568 | 569 | # from langchain_core.output_parsers import JsonOutputParser 570 | # prompt = ChatPromptTemplate.from_messages([ 571 | # ("system", system_prompt), 572 | # ("user", "{input}")], 573 | # partial_variables={"format_instructions": parser.get_format_instructions()}) 574 | 575 | #inner_auto 576 | if st.session_state.global_task in ["Auto Selection", "Inner Auto Selection"]: 577 | user_input = None 578 | # col1, col2 = st.columns([0.75, 13]) - THIS WILL STOP AUTO-SCROLL 579 | if st.session_state.first_question and st.session_state.first_question[-1]["role"] == "initializer" and len(st.session_state.first_question) % 2 == 1: 580 | question = st.session_state.first_question[-1]["content"] 581 | else: 582 | question = None 583 | 584 | # if st.session_state.second_task or : 585 | try: 586 | user_input = None 587 | total_input = st.chat_input( 588 | key="inner_auto_selection", 589 | accept_file = "multiple", 590 | file_type = ["pdf", "jpg", "jpeg", "png", "doc", "docx"], 591 | placeholder="Enter your query for either Smart Web Search or Productivity Agent or Data Analysis Agent...", 592 | ) 593 | if total_input: 594 | if hasattr(total_input, 'text') and total_input.text: 595 | user_input = total_input.text 596 | else: 597 | user_input = "dummy" 598 | if hasattr(total_input, 'files') and total_input.files: 599 | files_collected = total_input['files'] 600 | try: 601 | chunk_count = process_pdf(files_collected) 602 | print(">>>>>>Embedding") 603 | print(chunk_count) 604 | except: 605 | pass 606 | else: 607 | files_collected = None 608 | 609 | try: 610 | print("new input") 611 | print(user_input) 612 | if not user_input: 613 | user_input = question 614 | if user_input: 615 | print(f'============ {user_input}') 616 | route_ans = supervisor_node(user_input) 617 | print(f'=========== {route_ans}') 618 | try: 619 | task = "Productivity Assistant" if route_ans == "literature" or files_collected else "sql agent" 620 | except: 621 | task = "Productivity Assistant" if route_ans == "literature" else "sql agent" 622 | 623 | print(">>>> Inner Auto Selection Final result:", route_ans) 624 | st.session_state.inner_auto = True 625 | except Exception as e: 626 | route_ans = "literature" 627 | print(f"Error Inner/Auto Selection: {e}") 628 | task = "Productivity Assistant" 629 | except Exception as e: 630 | print(f"Error Inner/Auto Selection: {e}") 631 | st.error(f"Error Inner/Auto Selection: {e}") 632 | route_ans = "literature" 633 | task = "Productivity Assistant" 634 | 635 | if task == "Productivity Assistant": 636 | # user_input = st.chat_input("Enter your Biomedical Queries for Coherent literature response...") 637 | stop_button = st.button("▣", key="stop_button", help="Stop the current task") 638 | try: 639 | if pdf_content: 640 | pass 641 | except UnboundLocalError: 642 | pdf_content = None 643 | 644 | try: 645 | if files_collected: 646 | pass 647 | except UnboundLocalError: 648 | files_collected = None 649 | 650 | if "vectorstore" not in st.session_state: 651 | st.session_state.vectorstore = None 652 | 653 | if (not st.session_state.first_question or st.session_state.inner_auto) and ('user_input' in locals() or 'user_input' in globals()):#new 654 | print("inner_loop") 655 | print(user_input) 656 | pass 657 | elif st.session_state.first_question and st.session_state.first_question[-1]["role"] == "initializer" and len(st.session_state.first_question) % 2 == 1: 658 | print("first_question") 659 | user_input = st.session_state.first_question[-1]["content"] 660 | files_collected = st.session_state.first_question[-1]["file"] 661 | if files_collected: 662 | chunk_count = process_pdf(files_collected) 663 | st.session_state.first_question[-1]["role"] = "processed" 664 | if st.session_state.global_task not in ["Auto Selection", "Inner Auto Selection"]:#new 665 | placeholder_ip = st.chat_input( 666 | key="literature_query", 667 | accept_file = "multiple", 668 | file_type = ["pdf", "jpg", "jpeg", "png", "doc", "docx"], 669 | placeholder="Enter your query for general-purpose AI model...", 670 | ) 671 | else: 672 | print("> not first_question") 673 | if st.session_state.global_task not in ["Auto Selection", "Inner Auto Selection"]:#new 674 | user_input = None 675 | total_input = st.chat_input( 676 | key="literature_query_continue", 677 | accept_file = "multiple", 678 | file_type = ["pdf", "jpg", "jpeg", "png", "doc", "docx"], 679 | placeholder="Enter your query for general-purpose AI model...", 680 | ) 681 | 682 | if total_input: 683 | if hasattr(total_input, 'text') and total_input.text: 684 | user_input = total_input.text 685 | else: 686 | user_input = "dummy" 687 | if hasattr(total_input, 'files') and total_input.files: 688 | files_collected = total_input['files'] 689 | try: 690 | chunk_count = process_pdf(files_collected) 691 | print(">>>>>>Embedding") 692 | print(chunk_count) 693 | except: 694 | pass 695 | else: 696 | files_collected = None 697 | try: 698 | if user_input: 699 | st.session_state.messages.append({"role": "user", "content": user_input}) 700 | 701 | assistant_avatar = process_avatar("./assets/logo.png") # Original in assets folder 702 | user_avatar = "./assets/logo.png" 703 | 704 | with st.chat_message("user", avatar=user_avatar): 705 | st.markdown(user_input) 706 | 707 | with st.chat_message("assistant", avatar=assistant_avatar): 708 | # First display PDF processing information if available 709 | if hasattr(st.session_state, "pdf_processed"): 710 | pdf_info = st.session_state.pdf_processed 711 | st.write(f"✅ **Successfully processed your PDF into {pdf_info['num_chunks']} knowledge chunks!**") 712 | with st.expander("📚 **Chunks Visualization** - gte-Qwen2-1.5B-instruct", expanded=False): 713 | st.write(f":bulb: *{pdf_info['fun_fact']}*") 714 | if pdf_info['num_chunks'] > 0: 715 | st.write(f"✅ **Added your PDF into VectorDB which might contains other documents of the session.**") 716 | st.write("Here's how your document was divided:") 717 | st.bar_chart(pdf_info['chunk_sizes']) 718 | st.markdown("---") 719 | heading_relevant_chunk_placeholder = st.empty() 720 | relevant_chunk_placeholder = st.empty() 721 | 722 | # Clear the PDF processed info to avoid showing it again 723 | del st.session_state.pdf_processed 724 | 725 | st.session_state["final_output"] = "" 726 | st.session_state["is_expanded"] = False 727 | st.session_state["full_response"] = "" 728 | st.session_state.stop_streaming = False 729 | start_time = time.time() 730 | # Render expander only during processing 731 | # if not st.session_state.processing_done: 732 | full_response = "" 733 | 734 | with st.expander(":bulb: **Hide Reasoning** - Model XYZ", expanded=True): 735 | thinking_placeholder = st.empty() 736 | final_placeholder = st.empty() 737 | 738 | if files_collected and st.session_state.vectorstore: 739 | embeddings = model_pdf #HuggingFaceEmbeddings(model_name=str(path_manager.model_paths["EMBEDDINGS_qwen"])) 740 | 741 | if any(keyword in user_input.lower() for keyword in ["abstract", "conclusion", "introduction"]): 742 | relevant_docs = st.session_state.vectorstore.similarity_search(user_input, k=8, 743 | fetch_k=20, # Fetch more documents to consider before selecting top 8 744 | lambda_mult=0.7 # Balance between relevance and diversity 745 | ) 746 | res = "" 747 | heading_relevant_chunk_placeholder.markdown(f"

Best {len(relevant_docs)} Relevant Chunks:

", unsafe_allow_html=True) 748 | for rank, doc in enumerate(relevant_docs, start=1): 749 | content = doc.page_content 750 | res += f"**Rank {rank}:**\n\n{content}\n\n---\n\n" 751 | relevant_chunk_placeholder.markdown(res) 752 | elif any(keyword in user_input.lower() for keyword in ["summarize", "summarise", "summary", "summarization", "explain", "simplify", "simple term"]): 753 | relevant_docs = st.session_state.vectorstore.similarity_search(user_input, k=20, # Number of documents to return 754 | fetch_k=40, # Fetch more documents, then select most diverse k 755 | lambda_mult=0.6 # 60% relevance with 40% diversity (lower is more diverse) 756 | ) 757 | res = "" 758 | heading_relevant_chunk_placeholder.markdown(f"

Best {len(relevant_docs)} Relevant Chunks:

", unsafe_allow_html=True) 759 | 760 | for rank, doc in enumerate(relevant_docs, start=1): 761 | content = doc.page_content 762 | res += f"**Rank {rank}:**\n\n{content}\n\n---\n\n" 763 | relevant_chunk_placeholder.markdown(res) 764 | else: 765 | relevant_docs = st.session_state.vectorstore.similarity_search(user_input, k=5, 766 | fetch_k=15, # Fetch more documents for better selection 767 | lambda_mult=0.7 # 0.7 balances relevance with diversity (higher = more relevance) 768 | ) 769 | res = "" 770 | heading_relevant_chunk_placeholder.markdown(f"

Best {len(relevant_docs)} Relevant Chunks:

", unsafe_allow_html=True) 771 | for rank, doc in enumerate(relevant_docs, start=1): 772 | content = doc.page_content 773 | res += f"**Rank {rank}:**\n\n{content}\n\n---\n\n" 774 | relevant_chunk_placeholder.markdown(res) 775 | 776 | 777 | pdf_context = "\n\n".join([doc.page_content for doc in relevant_docs]) 778 | st.session_state.pdf_context = pdf_context 779 | else: 780 | # pdf_context = "" # Ensure it's an empty string when no file is uploaded 781 | pass 782 | 783 | 784 | # Streaming logic 785 | in_thinking = True 786 | raw_question = user_input 787 | user_input += f"""Follow all the rules. **Note:** Always use single backticks `keyword` to highlight important keywords.(e.g. `AI Agents`, `LangGraph`, `1999`) 788 | **Rules:** 789 | - Always include atleast one Table since information can be better presented Factually using **Table** try to include **Table** for compact facts. 790 | - If required use triple dash like ('---') to seperate different sections for better mark-down layout 791 | - Use single or triple backticks `keyword` to highlight important keywords for easy reading and appeal. 792 | - First go through the reasoning steps using appropriate opening and closing tags like ... Actual Response. 793 | """ 794 | try: 795 | # Get pdf_context from session state instead of assuming it exists as a local variable 796 | pdf_context = st.session_state.get('pdf_context', '') 797 | if pdf_context: 798 | prompt = get_prompt(user_input, st.session_state.context, pdf_context, pdf=True) 799 | else: 800 | prompt = get_prompt(user_input, st.session_state.context) 801 | except Exception as e: 802 | st.error(f"Error processing PDF context: {e}") 803 | prompt = get_prompt(user_input, st.session_state.context) 804 | 805 | gc.collect() 806 | 807 | torch.cuda.empty_cache() 808 | device_id = 0 809 | # torch.cuda.set_device(device_id) 810 | 811 | if 'full_resoning' not in st.session_state: 812 | st.session_state.full_resoning = "" 813 | else: 814 | st.session_state.full_resoning = "" 815 | 816 | # for chunk in llm_literature.stream(prompt):#, max_tokens=8000, top_k=top_k, top_p=top_p, repeat_penalty=repeat_penalty, temperature=temperature, stream=True): 817 | # if st.session_state.stop_streaming: 818 | # st.session_state.stop_streaming = True 819 | # break 820 | 821 | # chunk_text = chunk # chunk.content 822 | # st.session_state.full_response += chunk_text 823 | 824 | # if in_thinking: 825 | # thinking_placeholder.markdown(st.session_state.full_response + "▌") 826 | # st.session_state.full_resoning += chunk_text 827 | 828 | # if "" in st.session_state.full_response.lower(): 829 | # in_thinking = False 830 | # parts = st.session_state.full_response.split("", 1) 831 | # st.session_state.final_output = parts[1] if len(parts) > 1 else "" 832 | # # st.session_state.reasoning = parts[0].replace("", "") 833 | # st.session_state.full_response = parts[0].replace("", "") 834 | # st.session_state.is_expanded = False 835 | # # st.session_state.processing_done = True 836 | # time.sleep(0.05) # Helps sync state changes 837 | 838 | # if not in_thinking and chunk_text != "": 839 | # st.session_state.final_output += chunk_text 840 | # final_placeholder.markdown(st.session_state.final_output) 841 | 842 | # # Add timing stats 843 | # execution_time = time.time() - start_time 844 | # ntokens = len(st.session_state.full_response.split()) 845 | # timing_stats = f'Literature ⏳ {execution_time:.2f}s | {(execution_time/ntokens):.3f}s/token | {round((ntokens/execution_time),1)} tokens/s' 846 | # final_placeholder.markdown(f"{st.session_state.final_output}
{timing_stats}", unsafe_allow_html=True) 847 | 848 | # # Update conversation history 849 | # st.session_state.messages.append({"role": "assistant", "reasoning": st.session_state.full_resoning, "content": st.session_state.final_output}) 850 | 851 | 852 | st.markdown(""" 853 | """, unsafe_allow_html=True) 876 | except Exception as e: 877 | print("Except literature block:") 878 | print(st.session_state.global_task) 879 | import traceback 880 | def get_clean_error_message(e: Exception) -> str: 881 | error_type = type(e).__name__ 882 | error_msg = str(e) 883 | tb_last = "".join(traceback.format_exception_only(type(e), e)) 884 | return f"{error_type}: {error_msg}\n{tb_last}" 885 | st.markdown( 886 | f"Error details: {get_clean_error_message(e)}\n" 887 | ) 888 | 889 | # productivity agent 890 | else: 891 | if (not st.session_state.first_question or st.session_state.inner_auto) and ('user_input' in locals() or 'user_input' in globals()):#new 892 | print("<<<<<<<<<< inner_loop_productivity_db") 893 | print(user_input) 894 | question = user_input 895 | pass 896 | elif st.session_state.first_question and st.session_state.first_question[-1]["role"] == "initializer" and len(st.session_state.first_question) % 2 == 1: 897 | user_input = None #new 898 | question = st.session_state.first_question[-1]["content"] 899 | st.session_state.first_question[-1]["role"] = "processed" 900 | if st.session_state.global_task not in ["Auto Selection", "Inner Auto Selection"]:#new 901 | placeholder_ip = st.chat_input(f"Enter your {db_name} data related Query...") 902 | else: 903 | user_input = None #new 904 | if st.session_state.global_task not in ["Auto Selection", "Inner Auto Selection"]:#new 905 | question = st.chat_input(f"Enter your {db_name} data related Query...") 906 | stop_button = st.button("▣", key="stop_button", help="Stop the current task") 907 | # # Add user message to chat history 908 | if question: 909 | st.session_state.messages.append({"role": "user", "content": question}) 910 | assistant_avatar = process_avatar("./assets/logo.png") # Original in assets folder 911 | user_avatar = "./assets/logo.png" 912 | 913 | with st.chat_message("user", avatar=user_avatar): 914 | st.markdown(question) 915 | # with st.chat_message("assistant", avatar=assistant_avatar): 916 | 917 | TEMP, TEMP2 = 0, 0 918 | if st.session_state.global_task in ["Auto Selection", "Inner Auto Selection"]: 919 | print("if block agent") 920 | st.session_state.second_task = True 921 | print(st.session_state.global_task) 922 | print(st.session_state.second_task) 923 | # st.rerun() 924 | else: 925 | print(st.session_state.global_task) 926 | print('else block agent') 927 | except Exception as e: 928 | st.markdown(f'{str(e)}') 929 | 930 | if __name__ == "__main__": 931 | main() 932 | --------------------------------------------------------------------------------