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import os |
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os.environ["HF_HUB_TIMEOUT"] = "60" |
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import streamlit as st |
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import streamlit.components.v1 as components |
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from backend import process_medical_query, docs_cache |
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from visualization import create_medical_graph |
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def main(): |
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st.title("AI-Powered Medical Knowledge Graph Assistant") |
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st.markdown( |
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"**Using BioGPT-Large-PubMedQA + PubMed + Chroma** for advanced retrieval-augmented generation." |
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) |
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user_query = st.text_input("Enter biomedical/medical query", "Malaria and cough treatment") |
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if st.button("Submit"): |
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with st.spinner("Generating answer..."): |
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final_answer, sub_questions, initial_answer, critique = process_medical_query(user_query) |
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st.subheader("Sub-Question Decomposition") |
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st.write(sub_questions) |
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st.subheader("Initial AI Answer") |
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st.write(initial_answer) |
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st.subheader("Self-Critique") |
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st.write(critique) |
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st.subheader("Refined AI Answer") |
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st.write(final_answer) |
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st.subheader("Knowledge Graph") |
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docs = docs_cache.get(user_query, []) |
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if docs: |
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graph_html = create_medical_graph(user_query, docs) |
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components.html(graph_html, height=600, scrolling=True) |
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else: |
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st.info("No documents to visualize.") |
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if __name__ == "__main__": |
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main() |
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