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| import streamlit as st | |
| import os | |
| from langchain_groq import ChatGroq | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from dotenv import load_dotenv | |
| from PyPDF2 import PdfReader | |
| import tempfile | |
| load_dotenv() | |
| groq_api_key = os.getenv('GROQ_API_KEY') | |
| st.title("🔬 Health Report Analyzer") | |
| st.markdown("Quick analysis of your health reports") | |
| st.error("**Note:** Always consult healthcare professionals for medical advice") | |
| llm = ChatGroq( | |
| groq_api_key=groq_api_key, | |
| model_name="Llama3-8b-8192", | |
| temperature=0.3, | |
| max_tokens=2048 | |
| ) | |
| analysis_prompt = ChatPromptTemplate.from_template( | |
| """Analyze this report section briefly. Provide key insights in 2-3 sentences maximum. | |
| Report Section: {report_content} | |
| Question: {user_input} | |
| Give a direct, focused response:""" | |
| ) | |
| def extract_pdf_text(pdf_file): | |
| try: | |
| with tempfile.NamedTemporaryFile(delete=False) as tmp_file: | |
| tmp_file.write(pdf_file.getvalue()) | |
| tmp_file.seek(0) | |
| pdf_reader = PdfReader(tmp_file.name) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| except Exception as e: | |
| st.error(f"Error processing PDF: {str(e)}") | |
| return None | |
| finally: | |
| if 'tmp_file' in locals(): | |
| os.unlink(tmp_file.name) | |
| def chunk_text(text, chunk_size=1000): | |
| words = text.split() | |
| chunks = [] | |
| current_chunk = [] | |
| current_length = 0 | |
| for word in words: | |
| current_length += len(word) + 1 | |
| if current_length > chunk_size: | |
| chunks.append(' '.join(current_chunk)) | |
| current_chunk = [word] | |
| current_length = len(word) | |
| else: | |
| current_chunk.append(word) | |
| if current_chunk: | |
| chunks.append(' '.join(current_chunk)) | |
| return chunks | |
| def analyze_chunk(chunk, question): | |
| try: | |
| formatted_prompt = analysis_prompt.format( | |
| report_content=chunk, | |
| user_input=question | |
| ) | |
| response = llm.invoke(formatted_prompt) | |
| return response.content | |
| except Exception as e: | |
| return f"Error analyzing this section: {str(e)}" | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| if "report_chunks" not in st.session_state: | |
| st.session_state.report_chunks = None | |
| uploaded_file = st.file_uploader("Upload health report (PDF)", type="pdf") | |
| if uploaded_file: | |
| with st.spinner("Processing..."): | |
| report_text = extract_pdf_text(uploaded_file) | |
| if report_text: | |
| st.session_state.report_chunks = chunk_text(report_text) | |
| st.success("Report processed!") | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| if prompt := st.chat_input("Ask about your report"): | |
| if not st.session_state.report_chunks: | |
| st.warning("Please upload a report first!") | |
| else: | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| with st.chat_message("assistant"): | |
| with st.spinner("Analyzing..."): | |
| analyses = [analyze_chunk(chunk, prompt) for chunk in st.session_state.report_chunks] | |
| final_response = " ".join(filter(None, analyses)) | |
| st.markdown(final_response) | |
| st.session_state.messages.append({"role": "assistant", "content": final_response}) | |
| with st.sidebar: | |
| st.markdown(""" | |
| ### 📊 Quick Guide | |
| - Lab results | |
| - Vital signs | |
| - Test results | |
| - Measurements | |
| """) |