app.py

from flask import Flask, request, jsonify
from flask_cors import CORS
from PIL import Image
import io
import os

from transformers import DonutProcessor, VisionEncoderDecoderModel
import torch
import fitz  # PyMuPDF

# Initialize Flask
app = Flask(__name__)
CORS(app)

# Load Donut model and processor
device = "cpu"
processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base")
model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base").to(device)
model.eval()

def convert_pdf_to_image(file_stream):
    doc = fitz.open(stream=file_stream.read(), filetype="pdf")
    page = doc.load_page(0)
    pix = page.get_pixmap(dpi=150)
    img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
    return img

@app.route("/ocr", methods=["POST"])
def ocr():
    if "file" not in request.files:
        return jsonify({"error": "No file uploaded"}), 400

    file = request.files["file"]
    filename = file.filename.lower()

    # Convert input to PIL image
    if filename.endswith(".pdf"):
        image = convert_pdf_to_image(file)
    else:
        image = Image.open(io.BytesIO(file.read())).convert("RGB")

    # Preprocess image
    pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)

    # Run model
    with torch.no_grad():
        output = model.generate(pixel_values, max_length=512, return_dict_in_generate=True)

    # Decode output
    parsed_text = processor.batch_decode(output.sequences)[0]
    parsed_text = processor.tokenizer.decode(output.sequences[0], skip_special_tokens=True)

    return jsonify({"text": parsed_text})


@app.route("/", methods=["GET"])
def index():
    return "Smart OCR Flask API (Donut-based)"

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)