app.py

from flask import Flask, request, jsonify
from transformers import pipeline
from PIL import Image
import io
import fitz  # PyMuPDF
import os
from werkzeug.utils import secure_filename

app = Flask(__name__)

# Load model and processor using pipeline
model_name = "AsmaaElnagger/Diabetic_RetinoPathy_detection"
classifier = pipeline("image-classification", model=model_name)


# PDF to image conversion
def pdf_to_images_pymupdf(pdf_data):
    try:
        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
        images = []
        for page_num in range(pdf_document.page_count):
            page = pdf_document.load_page(page_num)
            pix = page.get_pixmap()
            img_data = pix.tobytes("jpeg")
            images.append(img_data)
        return images
    except Exception as e:
        print(f"Error converting PDF: {e}")
        return None


# File classification function (modified for API)
def classify_file(file_path):
    try:
        file_ext = os.path.splitext(file_path)[-1].lower()
        if file_ext in ['.jpg', '.jpeg', '.png', '.gif']:
            # Handle image upload
            image = Image.open(file_path).convert("RGB")
            result = classifier(image)[0]  # Get the top prediction
            return {
                "prediction": result["label"],
                "confidence": result["score"] * 100,
            }
        elif file_ext == '.pdf':
            # Handle PDF upload
            with open(file_path, "rb") as f:
                pdf_data = f.read()
            images = pdf_to_images_pymupdf(pdf_data)
            if images:
                image = Image.open(io.BytesIO(images[0])).convert("RGB")
                result = classifier(image)[0]  # Get the top prediction
                return {
                    "prediction": result["label"],
                    "confidence": result["score"] * 100,
                }
            else:
                return {"error": "PDF conversion failed."}
        else:
            return {"error": "Unsupported file type."}
    except Exception as e:
        return {"error": f"An error occurred: {e}"}


# API endpoint for file classification
@app.route('/classify', methods=['POST'])
def classify():
    if 'file' not in request.files:
        return jsonify({"error": "No file part"}), 400
    file = request.files['file']
    if file.filename == '':
        return jsonify({"error": "No file selected"}), 400

    filename = secure_filename(file.filename)
    filepath = os.path.join('/tmp', filename)  # Save to a temporary location
    file.save(filepath)
    result = classify_file(filepath)
    os.remove(filepath)  # remove temp file
    return jsonify(result), 200  # Return JSON response


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)