wehub-resource-sync
56 lines
2.3 KiB
Python
56 lines
2.3 KiB
Python
""" This example provides a basic framework to build a Chatbot UI interface in conjunction with LLMWare
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using Streamlit Chat UI.
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To run this example requires an install of Streamlit, e.g., `pip3 install streamlit`
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To execute the script, run from the command line with: `streamlit run using_with_streamlit_ui.py`
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Also, please note that the first time you run with a new model, the model will be downloaded and cached locally,
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so expect a delay on the 'first run' which will be much faster on every successive run.
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All components of the chatbot will be running locally, so the speed will be determined greatly by the
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CPU/GPU capacities of your machine.
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We have set the max_output at 250 tokens - for faster, set lower ...
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For more information on the Streamlit Chat UI,
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see https://docs.streamlit.io/develop/tutorials/llms/build-conversational-apps
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"""
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import streamlit as st
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from llmware.models import ModelCatalog
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# Title of the Streamlit app
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st.title("SLIM Extract Tool LLMWARE")
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# Text input for the text to analyze
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text_to_analyze = st.text_area("Enter the text to analyze:", "\"Good Will Hunting,\" a 1997 film directed by Gus Van Sant, tells the story of a young janitor at MIT who has a hidden talent for mathematics and undergoes therapy to confront his troubled past.")
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# Text input for the queries
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queries_input = st.text_area("Enter your queries (comma separated):", "Director, Film Name")
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# Convert the input queries to a list
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queries_list = [query.strip() for query in queries_input.split(',')]
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# Button to run the analysis
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if st.button("Analyze"):
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# Load the model
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model = ModelCatalog().load_model("slim-extract-tool", sample=False, temperature=0.0, max_output=250)
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# Initialize the output dictionary
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output_dict = {}
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# Loop through the queries and call the model with the entire text for each query
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for j, query in enumerate(queries_list):
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st.write(f"Query {j+1}: {query}")
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response = model.function_call(text_to_analyze, function="extract", params=[query])
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output_dict.update(response["llm_response"])
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#if not response["llm_response"]:
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# st.write("No response")
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# st.write("Extract response: ", response["llm_response"])
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# Display the response on the screen
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st.write("Output Dictionary:")
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st.json(output_dict) |