Delete app.py

app.py

@@ -1,187 +0,0 @@
-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()