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import streamlit as st |
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from transformers import pipeline |
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from concurrent.futures import ProcessPoolExecutor |
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prompt_template = ( |
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"<|system|>\n" |
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"You are a friendly chatbot who always gives helpful, detailed, and polite answers.</s>\n" |
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"<|user|>\n" |
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"{input_text}</s>\n" |
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"<|assistant|>\n" |
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) |
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def generate_base_response(input_text): |
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base_pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", max_length=512) |
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return base_pipe(input_text)[0]["generated_text"] |
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def generate_irai_response(input_text): |
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irai_pipe = pipeline("text-generation", model="InvestmentResearchAI/LLM-ADE_tiny-v0.001", max_length=512) |
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return irai_pipe(prompt_template.format(input_text=input_text))[0]["generated_text"].split("<|assistant|>")[1].strip() |
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def generate_response(input_text): |
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with ProcessPoolExecutor() as executor: |
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try: |
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future_base = executor.submit(generate_base_response, input_text) |
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future_irai = executor.submit(generate_irai_response, input_text) |
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base_resp = future_base.result() |
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irai_resp = future_irai.result() |
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except Exception as e: |
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st.error(f"An error occurred: {e}") |
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return None, None |
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return base_resp, irai_resp |
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st.title("IRAI LLM-ADE Model vs Base Model") |
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user_input = st.text_area("Enter a financial question:", "") |
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if st.button("Generate"): |
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if user_input: |
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base_response, irai_response = generate_response(user_input) |
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col1, col2 = st.columns(2) |
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with col1: |
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st.header("Base Model Response") |
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st.text_area("", base_response, height=300) |
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with col2: |
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st.header("IRAI LLM-ADE Model Response") |
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st.text_area("", irai_response, height=300) |
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else: |
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st.warning("Please enter some text to generate a response.") |
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