app.py

import streamlit as st
import base64
import requests
from PIL import Image, ImageDraw
from io import BytesIO
import fitz  # PyMuPDF
import time

# Configuration - Get API key from Streamlit secrets
GEMINI_API_KEY = st.secrets["GEMINI_API_KEY"]
GEMINI_MODEL = "gemini-2-flash"
DOCUMENT_TYPES = ["Land Records", "Caste Certificates", "Property Registrations"]

# Initialize session state
def initialize_session_state():
    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
    if "uploaded_file" not in st.session_state:
        st.session_state["uploaded_file"] = None

# Reset session state
def reset_session_state():
    for key in ["chat_history", "processed_doc", "doc_preview", "uploaded_file"]:
        st.session_state.pop(key, None)

# Encode uploaded file to base64
def encode_file(uploaded_file):
    try:
        file_bytes = uploaded_file.getvalue()
        
        if uploaded_file.type == "application/pdf":
            pdf = fitz.open(stream=BytesIO(file_bytes))
            page = pdf[0]
            pix = page.get_pixmap()
            img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
        elif uploaded_file.type.startswith('image/'):
            img = Image.open(BytesIO(file_bytes))
        elif uploaded_file.type == "text/plain":
            text = file_bytes.decode('utf-8')
            img = Image.new('RGB', (800, 600), color=(73, 109, 137))
            d = ImageDraw.Draw(img)
            d.text((10, 10), text, fill=(255, 255, 0))
        else:
            st.error("Unsupported file format")
            return None

        img_byte_arr = BytesIO()
        img.save(img_byte_arr, format='JPEG')
        return base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
    except Exception as e:
        st.error(f"File processing error: {str(e)}")
        return None

# Query Gemini API
def query_gemini(prompt, image_b64=None):
    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
        )
        if response.status_code != 200:
            st.error(f"API Request failed with status code: {response.status_code}")
            return None
        
        data = response.json()
        if 'error' in data:
            st.error(f"API Error: {data['error'].get('message', 'Unknown error')}")
            return None
        
        if not data.get('candidates'):
            st.error("No response candidates found in API response")
            return None
            
        candidate = data['candidates'][0]
        return candidate.get('content', {}).get('parts', [{}])[0].get('text', 'No response text found')
    except requests.exceptions.RequestException as e:
        st.error(f"API Request failed: {str(e)}")
        return None
    except Exception as e:
        st.error(f"Unexpected error: {str(e)}")
        return None

# Process the uploaded document
def process_document():
    if not st.session_state.uploaded_file:
        st.error("Please upload a document first.")
        return
    try:
        with st.spinner("Analyzing document..."):
            image_b64 = encode_file(st.session_state.uploaded_file)
            if not image_b64:
                return

            classify_prompt = f"Classify this document into one of these categories: {', '.join(DOCUMENT_TYPES)}. Respond only with the category name."
            doc_type = query_gemini(classify_prompt, image_b64)
            
            extract_prompt = """Extract key details including:
            - Names
            - Dates
            - Identification numbers
            - Locations
            Format as a bullet-point list."""
            details = query_gemini(extract_prompt, image_b64)
            
            verify_prompt = "Analyze this document for signs of tampering. Provide verification status."
            verification = query_gemini(verify_prompt, image_b64)

            st.session_state.processed_doc = {
                "type": doc_type or "Unclassified",
                "details": details or "No details extracted",
                "verification": verification or "Verification failed",
            }
            st.success("Document processing complete!")
            time.sleep(1)
    except Exception as e:
        st.error(f"Document processing failed: {str(e)}")
        st.session_state.processed_doc = None

# Main app function
def main():
    st.set_page_config(page_title="DocVerify AI", layout="wide")
    initialize_session_state()
    
    st.sidebar.header("Document Controls")
    st.sidebar.file_uploader("Upload Document", type=["pdf", "jpg", "jpeg", "png", "txt"], key="uploaded_file", on_change=process_document)
    
    if st.sidebar.button("New Document"):
        reset_session_state()
        st.rerun()
    
    st.title("DocVerify AI - Document Analysis")
    
    if st.session_state.processed_doc:
        st.subheader("Document Summary")
        st.markdown(f"**Type:** {st.session_state.processed_doc['type']}")
        st.markdown(f"**Verification Status:** {st.session_state.processed_doc['verification']}")
        st.text_area("Extracted Details", st.session_state.processed_doc['details'], height=200)

if __name__ == "__main__":
    main()