Create app.py

app.py

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+import streamlit as st
+import base64
+import requests
+from PIL import Image
+from io import BytesIO
+from pdf2image import convert_from_bytes
+
+# Configuration - Replace with your API key
+GEMINI_API_KEY = st.secrets["GEMINI_API_KEY"]
+GEMINI_MODEL = "gemini-2.0-flash"
+
+DOCUMENT_TYPES = [
+    "Insurance Policies", "Explanation of Benefits (EOBs)",
+    "Claims (Approved, Denied, or Pending)", "Visit Summaries",
+    "Test Results (Lab Reports, Imaging Reports)", 
+    "Prescriptions (E-Prescriptions, Handwritten)",
+    "Discharge Summaries", "Medical Bills", "Payment Statements",
+    "Pharmacy Receipts", "Prior Authorization Requests",
+    "Consent Forms", "Referral Letters", "Others"
+]
+
+def initialize_session_state():
+    """Initialize all session state variables"""
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+    if "processed_doc" not in st.session_state:
+        st.session_state.processed_doc = None
+    if "doc_preview" not in st.session_state:
+        st.session_state.doc_preview = None
+
+def encode_file(uploaded_file):
+    """Safely encode different file types to base64"""
+    try:
+        file_bytes = uploaded_file.getvalue()
+        
+        if uploaded_file.type == "application/pdf":
+            images = convert_from_bytes(file_bytes, first_page=1, last_page=1)
+            if not images:
+                raise ValueError("Failed to convert PDF to image")
+            img_byte_arr = BytesIO()
+            images[0].save(img_byte_arr, format='JPEG')
+            return base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
+        
+        return base64.b64encode(file_bytes).decode('utf-8')
+    except Exception as e:
+        st.error(f"File processing error: {str(e)}")
+        return None
+
+def query_gemini(prompt, image_b64=None):
+    """Handle Gemini API communication"""
+    url = f"https://generativelanguage.googleapis.com/v1/models/{GEMINI_MODEL}:generateContent?key={GEMINI_API_KEY}"
+    
+    parts = [{"text": prompt}]
+    if image_b64:
+        parts.append({
+            "inline_data": {
+                "mime_type": "image/jpeg",
+                "data": image_b64
+            }
+        })
+    
+    try:
+        response = requests.post(
+            url,
+            json={"contents": [{"parts": parts}]},
+            headers={"Content-Type": "application/json"},
+            timeout=30
+        )
+        response.raise_for_status()
+        return response.json()["candidates"][0]["content"]["parts"][0]["text"]
+    except Exception as e:
+        st.error(f"API Error: {str(e)}")
+        return None
+
+def process_document():
+    """Handle document processing pipeline"""
+    uploaded_file = st.session_state.uploaded_file
+    if not uploaded_file:
+        return
+    
+    try:
+        with st.spinner("Analyzing document..."):
+            # Convert to base64
+            image_b64 = encode_file(uploaded_file)
+            if not image_b64:
+                return
+
+            # Generate preview
+            if uploaded_file.type == "application/pdf":
+                images = convert_from_bytes(uploaded_file.getvalue(), first_page=1, last_page=1)
+                st.session_state.doc_preview = images[0]
+            else:
+                st.session_state.doc_preview = Image.open(uploaded_file)
+
+            # Classify document
+            classify_prompt = f"Classify this healthcare document into one of: {DOCUMENT_TYPES}. Respond only with the category name."
+            doc_type = query_gemini(classify_prompt, image_b64) or "Others"
+            
+            # Store results
+            st.session_state.processed_doc = {
+                "type": doc_type,
+                "content": image_b64,
+                "summary": query_gemini("Create a detailed structured summary of this healthcare document.", image_b64)
+            }
+            
+    except Exception as e:
+        st.error(f"Processing failed: {str(e)}")
+        st.session_state.processed_doc = None
+
+def handle_chat_query():
+    """Process user chat input"""
+    user_input = st.session_state.chat_input
+    if not user_input or not st.session_state.processed_doc:
+        return
+    
+    prompt = f"""
+    Document Context:
+    - Type: {st.session_state.processed_doc['type']}
+    - Summary: {st.session_state.processed_doc['summary']}
+    
+    Question: {user_input}
+    
+    Answer concisely and factually. If unsure, state "Information not found".
+    """
+    
+    with st.spinner("Generating response..."):
+        response = query_gemini(prompt, st.session_state.processed_doc['content']) 
+    
+    st.session_state.chat_history.append(("user", user_input))
+    st.session_state.chat_history.append(("assistant", response or "Could not generate response"))
+
+# UI Layout
+def main():
+    st.set_page_config(page_title="Healthcare Document Assistant", layout="wide")
+    initialize_session_state()
+
+    # Sidebar Section
+    with st.sidebar:
+        st.header("Document Management")
+        
+        # Preview above upload button
+        if st.session_state.doc_preview:
+            st.subheader("Preview")
+            st.image(st.session_state.doc_preview, use_container_width=True)
+        
+        # Upload button
+        st.file_uploader(
+            "Upload Document",
+            type=["pdf", "png", "jpg", "jpeg"],
+            key="uploaded_file",
+            on_change=process_document
+        )
+        
+        # Document type below upload button
+        if st.session_state.processed_doc:
+            st.divider()
+            st.subheader("Document Type")
+            st.markdown(f"**{st.session_state.processed_doc['type']}**")
+
+    # Main Content
+    st.title("Healthcare Document Assistant")
+    
+    if st.session_state.processed_doc:
+        # Document Summary
+        st.subheader("Document Summary")
+        st.markdown(st.session_state.processed_doc['summary'])
+        
+        # Chat Interface
+        st.divider()
+        st.subheader("Document Q&A")
+        
+        # Chat history
+        for role, message in st.session_state.chat_history:
+            with st.chat_message(role.capitalize()):
+                st.markdown(message)
+        
+        # Chat input
+        st.chat_input(
+            "Ask about the document...",
+            key="chat_input",
+            on_submit=handle_chat_query
+        )
+    else:
+        st.info("Please upload a document to begin analysis")
+
+if __name__ == "__main__":
+    main()