├── logo.png
├── cacheDIR.png
├── mystreamlitAPP.png
├── mystreamlitAPP-results.png
├── README.md
└── myapp.py


/logo.png:
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https://raw.githubusercontent.com/fabiomatricardi/llmwareSLIMS/main/logo.png


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/cacheDIR.png:
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https://raw.githubusercontent.com/fabiomatricardi/llmwareSLIMS/main/cacheDIR.png


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/mystreamlitAPP.png:
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https://raw.githubusercontent.com/fabiomatricardi/llmwareSLIMS/main/mystreamlitAPP.png


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/mystreamlitAPP-results.png:
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https://raw.githubusercontent.com/fabiomatricardi/llmwareSLIMS/main/mystreamlitAPP-results.png


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/README.md:
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 1 | <img src="https://github.com/fabiomatricardi/llmwareSLIMS/raw/main/logo.png" width=900>
 2 | 
 3 | # Go Open, go Lean: LLMWare now can boost your AI-powered enterprise.
 4 | ### Budget-friendly Efficiency: LLMWare makes AI Back-Office dreams a Reality with a herd of new SLIMs models.
 5 | Repo of the code from the Medium article
 6 | 
 7 | This repo will work only with python version < 3.12
 8 | 
 9 | Create a new directory and a virtual environment
10 | 
11 | ```
12 | mkdir llmware
13 | cd llmware
14 | python -m venv venv
15 | ```
16 | 
17 | Activate the venv and install the following packages
18 | 
19 | ```
20 | source venv/bin/activate  #activate the venv on Mac/Linux
21 | venv\Scripts\activate  #activate the venv on Windows
22 | pip install llmware
23 | pip install streamlit
24 | ```
25 | 
26 | 
27 | Download then 2 files in the project directory:
28 | - myapp.py
29 | - logo.png
30 | 
31 | In the terminal, the the venv active run the following command
32 | ```
33 | streamlit run myapp.py
34 | ```
35 | 
36 | 
37 | During the first execution the application will download the models on your local machine <br>
38 | in the cache HuggingFace directory, something like this:
39 | 
40 | ```
41 | C:\Users\User\.cache\huggingface\hub   for the non quantized models
42 | C:\Users\User\llmware_data\model_repo  for the GGUF files
43 | ```
44 | 
45 | <img src="https://github.com/fabiomatricardi/llmwareSLIMS/raw/main/cacheDIR.png" width=700>
46 | 
47 | 
48 | 
49 | 


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/myapp.py:
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 1 | # Sources
 2 | # https://github.com/llmware-ai/llmware/blob/main/examples/SLIM-Agents/agent-multistep-analysis.py
 3 | # https://www.youtube.com/watch?v=0MOMBJjytkQ by AI Anytime
 4 | 
 5 | import streamlit as st
 6 | from llmware.models import ModelCatalog
 7 | from llmware.prompts import Prompt
 8 | 
 9 | def perform_sum(text):
10 |     #nli_model = ModelCatalog().load_model("slim-nli-tool")
11 |     prompter = Prompt().load_model("llmware/bling-tiny-llama-v0")
12 |     instruction = "What is a brief summary?"
13 |     response_sum = prompter.prompt_main(instruction, context=text)
14 |     return response_sum
15 | 
16 | def tags(text):
17 |     tags_model = ModelCatalog().load_model("slim-tags-tool")
18 |     response_tags = tags_model.function_call(text, get_logits=False)
19 |     return response_tags
20 | 
21 | def topics(text):
22 |     topics_model = ModelCatalog().load_model("slim-topics-tool")
23 |     response_topics = topics_model.function_call(text, get_logits=False)
24 |     return response_topics
25 | 
26 | def intent(text):
27 |     intent_model = ModelCatalog().load_model("slim-intent-tool")
28 |     response_intent = intent_model.function_call(text, get_logits=False)
29 |     return response_intent
30 | 
31 | def category(text):
32 |     category_model = ModelCatalog().load_model("slim-category-tool")
33 |     response_category = category_model.function_call(text, get_logits=False)
34 |     return response_category
35 | 
36 | def ner(text):
37 |     ner_model = ModelCatalog().load_model("slim-ner-tool")
38 |     response_ner = ner_model.function_call(text, get_logits=False)
39 |     return response_ner
40 | 
41 | 
42 | # Streamlit app layout
43 | st.image('logo.png',use_column_width="auto")
44 | st.title("Intensive Enterprise NLP Tasks")
45 | st.markdown("### using only CPU resources")
46 | 
47 | # Text input
48 | text = st.text_area("Enter text here:")
49 | 
50 | # Analysis tools selection
51 | analysis_tools = st.multiselect(
52 |     "Select the analysis tools to use:",
53 |     ["Generate Tags", "Identify Topics",
54 |      "Perform Intent", "Get Category",
55 |      "Perform NER", "Perform Summarization"],
56 |     ["Generate Tags"]  # Default selection
57 | )
58 | 
59 | # Run the selected TASKS/Agents and display results in plain json format
60 | if st.button("Analyze"):
61 |     results = {}
62 |     
63 |     if "Generate Tags" in analysis_tools:
64 |         results["Generate Tags"] = tags(text)
65 |     if "Identify Topics" in analysis_tools:
66 |         results["Identify Topics"] = topics(text)
67 |     if "Perform Intent" in analysis_tools:
68 |         results["Perform Intent"] = intent(text)
69 |     if "Get Category" in analysis_tools:
70 |         results["Get Category"] = category(text)
71 |     if "Perform NER" in analysis_tools:
72 |         results["Perform NER"] = ner(text)
73 |     if "Perform Summarization" in analysis_tools:
74 |         results["Perform Summarization"] = perform_sum(text)
75 |     
76 |     for tool, response in results.items():
77 |         st.subheader(tool)
78 |         st.json(response)


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