├── .gitignore ├── AIAssistant └── app.py ├── LICENSE ├── One-Prompt-Charts ├── README.md ├── app.py ├── app_brain.py ├── key_check.py └── utils.py ├── README.md ├── Stream-Argument ├── app.py └── requirenents.txt ├── chatbot └── app.py └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /AIAssistant/app.py: -------------------------------------------------------------------------------- 1 | import openai 2 | import streamlit as st 3 | 4 | openai.api_key = st.secrets['api_secret'] 5 | 6 | # This function uses the OpenAI Completion API to generate a 7 | # response based on the given prompt. The temperature parameter controls 8 | # the randomness of the generated response. A higher temperature will result 9 | # in more random responses, 10 | # while a lower temperature will result in more predictable responses. 11 | 12 | def generate_response(prompt): 13 | completions = openai.Completion.create ( 14 | engine="text-davinci-003", 15 | prompt=prompt, 16 | max_tokens=1024, 17 | n=1, 18 | stop=None, 19 | temperature=0.5, 20 | ) 21 | 22 | message = completions.choices[0].text 23 | return message 24 | 25 | st.title("AI Assistant : openAI + Streamlit") 26 | 27 | prompt = st.text_input("Enter your message:", key='prompt') 28 | if st.button("Submit", key='submit'): 29 | response = generate_response(prompt) 30 | st.success(response) 31 | 32 | 33 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Avra 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /One-Prompt-Charts/README.md: -------------------------------------------------------------------------------- 1 | ## One Prompt Charts 2 | 3 | - #### One Prompt Chart is available as a template over Databutton . Link to the template to get started right away. 4 | 5 | - Youtube video - here 6 | 7 | - *Blog post coming soon* 8 | 9 | - Live demo app (an extended version of this tutorial) - Link 10 | 11 | One-Prompt Charts uses OpenAI powered Large Language Models and Databutton for the ease of development. The app utilizes tools like LangChainAI and PandasAI for testing. This started as a fun project. Big thanks to Databutton for letting users use prompts for free. 12 | 13 | -------------------------------------------------------------------------------- /One-Prompt-Charts/app.py: -------------------------------------------------------------------------------- 1 | # Import necessary modules 2 | import databutton as db 3 | import streamlit as st 4 | 5 | # Import custom utility functions 6 | from utils import get_data 7 | from app_brain import handle_openai_query 8 | 9 | # This function checks for API keys and completely optional as usage 10 | # and is not necessary when defining an Input box or using secrets 11 | from key_check import key_check 12 | 13 | # Suppress deprecation warnings related to Pyplot's global use 14 | st.set_option("deprecation.showPyplotGlobalUse", False) 15 | 16 | 17 | # Cache the header of the app to prevent re-rendering on each load 18 | @st.cache_resource 19 | def display_app_header(): 20 | """Display the header of the Streamlit app.""" 21 | st.title("1️⃣ One-Prompt Charts 📊 ") 22 | st.markdown("***Prompt about your data, and see it visualized** ✨ This app runs on the power of your prompting. As here in Databutton HQ, we envision, '**Prompting is the new programming.**'*") 23 | 24 | 25 | # Display the header of the app 26 | display_app_header() 27 | 28 | with st.expander("App Overview", expanded=False): 29 | st.markdown( 30 | """ 31 | 32 | You will find each functions either in the library or in the main script. Feel free to modu 33 | - **App Header:** The function `display_app_header` defines the title and a brief description of the app, setting the context for the user. This function is displayed at the top of the app when called on line 24. 34 | 35 | - **API Key Check:** The `key_check` function is invoked to ensure the necessary API keys are present before proceeding. This might be for authentication or to access certain services. 36 | 37 | - **Data Upload and Display:** The app provides the user with an option to upload data using the `get_data` function (line 47). Once the data is uploaded, it's optionally displayed in an expandable section for the user to review. 38 | 39 | - **OpenAI Query Handling:** If the uploaded data is not empty, the `handle_openai_query` function is called (line 64) to process the user's prompt regarding the data and visualize it accordingly. If the uploaded data is empty, a warning is displayed to the user. 40 | 41 | """ 42 | ) 43 | 44 | # Check for the necessary API keys 45 | key_check() 46 | 47 | options = st.radio( 48 | "Data Usage", options=["Upload file", "Use Data in Storage"], horizontal=True 49 | ) 50 | if options == "Upload file": 51 | # Get data uploaded by the user 52 | df = get_data() 53 | else: 54 | df = db.storage.dataframes.get(key="spectra-csv") 55 | 56 | 57 | # If data is uploaded successfully 58 | if df is not None: 59 | # Create an expander to optionally display the uploaded data 60 | with st.expander("Show data"): 61 | st.write(df) 62 | 63 | # Extract column names for further processing 64 | column_names = ", ".join(df.columns) 65 | 66 | # Check if the uploaded DataFrame is not empty 67 | if not df.empty: 68 | # Handle the OpenAI query and display results 69 | handle_openai_query(df, column_names) 70 | else: 71 | # Display a warning if the uploaded data is empty 72 | st.warning("The given data is empty.") 73 | -------------------------------------------------------------------------------- /One-Prompt-Charts/app_brain.py: -------------------------------------------------------------------------------- 1 | import databutton as db 2 | import streamlit as st 3 | import pandas as pd 4 | import re 5 | import openai 6 | 7 | # Define the model to use 8 | MODEL_NAME = "gpt-3.5-turbo" 9 | 10 | 11 | def handle_openai_query(df, column_names): 12 | """ 13 | Handle the OpenAI query and display the response. 14 | 15 | Parameters: 16 | - df: DataFrame containing the data 17 | - column_names: List of column names in the DataFrame 18 | """ 19 | 20 | # Create a text area for user input 21 | query = st.text_area( 22 | "Enter your Prompt:", 23 | placeholder="Prompt tips: Use plotting related keywords such as 'Plots' or 'Charts' or 'Subplots'. Prompts must be concise and clear, example 'Bar plot for the first ten rows.'", 24 | help=""" 25 | How an ideal prompt should look like? *Feel free to copy the format and adapt to your own dataset.* 26 | 27 | ``` 28 | - Subplot 1: Line plot of the whole spectra. 29 | - Subplot 2: Zoom into the spectra in region 1000 and 1200. 30 | - Subplot 3: Compare the area of whole spectra and zoom spectra as Bar Plot. 31 | - Subplot 4: Area curve of the zoom spectra. 32 | ``` 33 | """, 34 | ) 35 | 36 | # If the "Get Answer" button is clicked 37 | if st.button("Get Answer"): 38 | # Ensure the query is not empty 39 | if query and query.strip() != "": 40 | # Define the prompt content 41 | prompt_content = f""" 42 | The dataset is ALREADY loaded into a DataFrame named 'df'. DO NOT load the data again. 43 | 44 | The DataFrame has the following columns: {column_names} 45 | 46 | Before plotting, ensure the data is ready: 47 | 1. Check if columns that are supposed to be numeric are recognized as such. If not, attempt to convert them. 48 | 2. Handle NaN values by filling with mean or median. 49 | 50 | Use package Pandas and Matplotlib ONLY. 51 | Provide SINGLE CODE BLOCK with a solution using Pandas and Matplotlib plots in a single figure to address the following query: 52 | 53 | {query} 54 | 55 | - USE SINGLE CODE BLOCK with a solution. 56 | - Do NOT EXPLAIN the code 57 | - DO NOT COMMENT the code. 58 | - ALWAYS WRAP UP THE CODE IN A SINGLE CODE BLOCK. 59 | - The code block must start and end with ``` 60 | 61 | - Example code format ```code``` 62 | 63 | - Colors to use for background and axes of the figure : #F0F0F6 64 | - Try to use the following color palette for coloring the plots : #8f63ee #ced5ce #a27bf6 #3d3b41 65 | 66 | """ 67 | 68 | # Define the messages for the OpenAI model 69 | messages = [ 70 | { 71 | "role": "system", 72 | "content": "You are a helpful Data Visualization assistant who gives a single block without explaining or commenting the code to plot. IF ANYTHING NOT ABOUT THE DATA, JUST politely respond that you don't know.", 73 | }, 74 | {"role": "user", "content": prompt_content}, 75 | ] 76 | 77 | # Call OpenAI and display the response 78 | with st.status("📟 *Prompting is the new programming*..."): 79 | with st.chat_message("assistant", avatar="📊"): 80 | botmsg = st.empty() 81 | response = [] 82 | for chunk in openai.ChatCompletion.create( 83 | model=MODEL_NAME, messages=messages, stream=True 84 | ): 85 | text = chunk.choices[0].get("delta", {}).get("content") 86 | if text: 87 | response.append(text) 88 | result = "".join(response).strip() 89 | botmsg.write(result) 90 | execute_openai_code(result, df, query) 91 | 92 | 93 | def extract_code_from_markdown(md_text): 94 | """ 95 | Extract Python code from markdown text. 96 | 97 | Parameters: 98 | - md_text: Markdown text containing the code 99 | 100 | Returns: 101 | - The extracted Python code 102 | """ 103 | # Extract code between the delimiters 104 | code_blocks = re.findall(r"```(python)?(.*?)```", md_text, re.DOTALL) 105 | 106 | # Strip leading and trailing whitespace and join the code blocks 107 | code = "\n".join([block[1].strip() for block in code_blocks]) 108 | 109 | return code 110 | 111 | 112 | def execute_openai_code(response_text: str, df: pd.DataFrame, query): 113 | """ 114 | Execute the code provided by OpenAI in the app. 115 | 116 | Parameters: 117 | - response_text: The response text from OpenAI 118 | - df: DataFrame containing the data 119 | - query: The user's query 120 | """ 121 | 122 | # Extract code from the response text 123 | code = extract_code_from_markdown(response_text) 124 | 125 | # If there's code in the response, try to execute it 126 | if code: 127 | try: 128 | exec(code) 129 | st.pyplot() 130 | except Exception as e: 131 | error_message = str(e) 132 | st.error( 133 | f"📟 Apologies, failed to execute the code due to the error: {error_message}" 134 | ) 135 | st.warning( 136 | """ 137 | 📟 Check the error message and the code executed above to investigate further. 138 | 139 | Pro tips: 140 | - Tweak your prompts to overcome the error 141 | - Use the words 'Plot'/ 'Subplot' 142 | - Use simpler, concise words 143 | - Remember, I'm specialized in displaying charts not in conveying information about the dataset 144 | """ 145 | ) 146 | else: 147 | st.write(response_text) -------------------------------------------------------------------------------- /One-Prompt-Charts/key_check.py: -------------------------------------------------------------------------------- 1 | import databutton as db 2 | import streamlit as st 3 | import openai 4 | from openai import OpenAI 5 | 6 | 7 | def is_valid_openai_key(api_key: str) -> bool: 8 | """ 9 | Validates whether the provided OpenAI API key is valid. 10 | 11 | Parameters: 12 | - api_key (str): The OpenAI API key to validate. 13 | 14 | Returns: 15 | - bool: True if the API key is valid, False otherwise. 16 | """ 17 | try: 18 | Client = OpenAI(api_key=db.secrets.get("OPENAI_API_KEY")) 19 | # Attempting to list models; will throw an exception if the key is invalid. 20 | if Client.models.list(): 21 | return True 22 | except: 23 | return False 24 | 25 | 26 | def key_check(): 27 | """ 28 | Checks the OpenAI API key, either from the Databutton secrets store or from user input. 29 | If the key is valid, it continues the app flow; otherwise, it stops the app and provides feedback. 30 | """ 31 | try: 32 | # Attempting to get the OpenAI API key from the Databutton secrets store. 33 | openai.api_key = db.secrets.get(name="OPENAI_API_KEY") 34 | 35 | # Check if the connection is established and models are available. 36 | if not openai.Model.list(): 37 | st.write("Not connected to OpenAI.") 38 | st.stop() 39 | 40 | except Exception as e: 41 | # Display information about needing an OpenAI API key. 42 | mtinfo = st.empty() 43 | mtinfo.info( 44 | """ 45 | Hi there! Welcome to the "One-Prompt Charts" app template. 📊 46 | 47 | This app allows you to upload your data and get visual insights with just a single prompt. However, to power the magic behind the scenes, I need your OpenAI API key. 48 | 49 | If you don't have a key, you can sign up and create one [here](https://platform.openai.com/account/api-keys). 50 | 51 | Don't worry, your key will be securely stored in the Databutton secrets store, which you can find in the left-side menu under "Configure". If you prefer to add it manually, ensure to assign the name as `OPENAI_API_KEY` for your secret. 52 | 53 | Once set up, simply upload your data, prompt about it, and see it visualized! ✨ 54 | 55 | """, 56 | icon="🤖", 57 | ) 58 | 59 | # Accept user input for the API key. 60 | mt = st.empty() 61 | user_provided_key = mt.text_input( 62 | "Type your OpenAI API key here to continue:", type="password" 63 | ) 64 | 65 | # Check the format of the provided API key. 66 | if user_provided_key.startswith("sk-"): 67 | with st.status("Connecting to OpenAI.", expanded=True) as status: 68 | # Validate the provided API key. 69 | if is_valid_openai_key(user_provided_key): 70 | status.write("Adding OpenAI API key...") 71 | db.secrets.put(name="OPENAI_API_KEY", value=user_provided_key) 72 | status.update( 73 | label="Added OpenAI API key to Databutton secrets securely. Chatbot is enabled for you.", 74 | state="complete", 75 | ) 76 | status.write("Added and cleaning onboarding UI...") 77 | # Clean the screen 78 | mt.empty() 79 | mtinfo.empty() 80 | else: 81 | st.error( 82 | "Error: Invalid OpenAI API Key. You can find your API key at [this link](https://platform.openai.com/account/api-keys).", 83 | ) 84 | st.stop() 85 | else: 86 | st.warning("Please ensure a correct API key.") 87 | st.stop() 88 | -------------------------------------------------------------------------------- /One-Prompt-Charts/utils.py: -------------------------------------------------------------------------------- 1 | import databutton as db 2 | import streamlit as st 3 | import pandas as pd 4 | 5 | 6 | def get_data(): 7 | """ 8 | Upload data via a file. 9 | 10 | Returns: 11 | - df: DataFrame containing the uploaded data or None if no data was uploaded 12 | """ 13 | 14 | # File uploader for data file 15 | file_types = ["csv", "xlsx", "xls"] 16 | data_upload = st.file_uploader("Upload a data file", type=file_types) 17 | 18 | if data_upload: 19 | # Check the type of file uploaded and read accordingly 20 | if data_upload.name.endswith('.csv'): 21 | df = pd.read_csv(data_upload) 22 | elif data_upload.name.endswith('.xlsx') or data_upload.name.endswith('.xls'): 23 | df = pd.read_excel(data_upload) 24 | else: 25 | df = None 26 | return df 27 | 28 | return None -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # OpenAI Web App tutorials 2 | 3 | - [OpenAI](https://platform.openai.com/docs/models) models 4 | 5 | - [Databutton](https://www.databutton.io) - App development and deployment 6 | 7 | - [Streamlit](https://streamlit.io) - The UI framework used by Databutton 8 | 9 | - [Playlist](https://youtube.com/playlist?list=PLqQrRCH56DH82KNwvlWpgh3YJXu461q69&si=Jt5UKCabu9vHEmyK) 10 | -------------------------------------------------------------------------------- /Stream-Argument/app.py: -------------------------------------------------------------------------------- 1 | import openai 2 | import streamlit as st 3 | from streamlit_pills import pills 4 | 5 | openai.api_key = st.secrets['api_secret'] 6 | 7 | st.subheader("AI Assistant : Streamlit + OpenAI: `stream` *argument*") 8 | selected = pills("", ["NO Streaming", "Streaming"], ["🎈", "🌈"]) 9 | 10 | user_input = st.text_input("You: ",placeholder = "Ask me anything ...", key="input") 11 | 12 | 13 | if st.button("Submit", type="primary"): 14 | st.markdown("----") 15 | res_box = st.empty() 16 | if selected == "Streaming": 17 | report = [] 18 | for resp in openai.Completion.create(model='text-davinci-003', 19 | prompt=user_input, 20 | max_tokens=120, 21 | temperature = 0.5, 22 | stream = True): 23 | # join method to concatenate the elements of the list 24 | # into a single string, 25 | # then strip out any empty strings 26 | report.append(resp.choices[0].text) 27 | result = "".join(report).strip() 28 | result = result.replace("\n", "") 29 | res_box.markdown(f'*{result}*') 30 | 31 | else: 32 | completions = openai.Completion.create(model='text-davinci-003', 33 | prompt=user_input, 34 | max_tokens=120, 35 | temperature = 0.5, 36 | stream = False) 37 | result = completions.choices[0].text 38 | 39 | res_box.write(result) 40 | st.markdown("----") 41 | -------------------------------------------------------------------------------- /Stream-Argument/requirenents.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | streamlit-pills==0.3.0 3 | openai 4 | -------------------------------------------------------------------------------- /chatbot/app.py: -------------------------------------------------------------------------------- 1 | import openai 2 | import streamlit as st 3 | from streamlit_chat import message 4 | 5 | openai.api_key = st.secrets['api_secret'] 6 | 7 | # This function uses the OpenAI Completion API to generate a 8 | # response based on the given prompt. The temperature parameter controls 9 | # the randomness of the generated response. A higher temperature will result 10 | # in more random responses, 11 | # while a lower temperature will result in more predictable responses. 12 | def generate_response(prompt): 13 | completions = openai.Completion.create ( 14 | engine="text-davinci-003", 15 | prompt=prompt, 16 | max_tokens=1024, 17 | n=1, 18 | stop=None, 19 | temperature=0.5, 20 | ) 21 | 22 | message = completions.choices[0].text 23 | return message 24 | 25 | 26 | st.title("🤖 chatBot : openAI GPT-3 + Streamlit") 27 | 28 | 29 | if 'generated' not in st.session_state: 30 | st.session_state['generated'] = [] 31 | 32 | if 'past' not in st.session_state: 33 | st.session_state['past'] = [] 34 | 35 | 36 | def get_text(): 37 | input_text = st.text_input("You: ","Hello, how are you?", key="input") 38 | return input_text 39 | 40 | 41 | user_input = get_text() 42 | 43 | if user_input: 44 | output = generate_response(user_input) 45 | st.session_state.past.append(user_input) 46 | st.session_state.generated.append(output) 47 | 48 | if st.session_state['generated']: 49 | 50 | for i in range(len(st.session_state['generated'])-1, -1, -1): 51 | message(st.session_state["generated"][i], key=str(i)) 52 | message(st.session_state['past'][i], is_user=True, key=str(i) + '_user') 53 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | streamlit-pills==0.3.0 3 | openai 4 | --------------------------------------------------------------------------------