app.py

import gradio as gr
import google.generativeai as genai
import base64
import io
from PIL import Image
import fitz  # PyMuPDF
import os

# Configure Gemini API
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
model = genai.GenerativeModel('gemini-1.5-flash')

# Prompt for Gemini
prompt = """
You are analyzing a medical document or an application form from patient.
Extract the following fields as JSON:
- Position applied for
- Office/Ministry
- Duty station
- First name(s)
- Surname
- Date of birth
- Gender
- Citizenship
- Postal Address
- Residential Address
- Email
- Phone number (mobile)
"""

def process_pdf(pdf_bytes: bytes):
    doc = fitz.open(stream=pdf_bytes, filetype="pdf")
    results = []

    for page_num in range(len(doc)):
        page = doc.load_page(page_num)
        pix = page.get_pixmap(dpi=200)

        # Convert to PIL image
        image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)

        # Convert to JPEG bytes
        buffered = io.BytesIO()
        image.save(buffered, format="JPEG")
        jpeg_bytes = buffered.getvalue()

        # Send to Gemini
        response = model.generate_content([
            prompt,
            {
                "mime_type": "image/jpeg",
                "data": jpeg_bytes
            }
        ])

        results.append(response.text.strip())

    return "\n\n---\n\n".join(results)

# Gradio interface
demo = gr.Interface(
    fn=process_pdf,
    inputs=gr.File(type="binary", label="Upload PDF Form"),
    outputs="textbox",
    title="Healthelic Form Data Extractor (PDF Scanner) - Gemini 1.5 Flash",
    description="Upload a scanned medical form in PDF format to extract key fields using Gemini 1.5 Flash."
)

if __name__ == "__main__":
    demo.launch()