app.py

from flask import Flask, render_template, request, redirect, url_for
from transformers import DetrImageProcessor, DetrForObjectDetection
from PIL import Image, ImageDraw
import torch
import os
import uuid
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)

# Set upload folder
UPLOAD_FOLDER = 'static/uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

# Load DETR model and processor
logger.info("Loading DETR model and processor...")
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
logger.info("Model and processor loaded successfully.")

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/upload', methods=['POST'])
def upload_file():
    if 'file' not in request.files:
        logger.warning("No file part in request.")
        return redirect(request.url)
    file = request.files['file']
    if file.filename == '':
        logger.warning("No file selected.")
        return redirect(request.url)
    
    try:
        # Save uploaded file
        filename = str(uuid.uuid4()) + os.path.splitext(file.filename)[1]
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(filepath)
        logger.info(f"File saved: {filename}")

        # Process image
        image = Image.open(filepath).convert("RGB")
        image = image.resize((800, 600))  # Resize for performance
        inputs = processor(images=image, return_tensors="pt")
        outputs = model(**inputs)
        
        # Post-process outputs
        target_sizes = torch.tensor([image.size[::-1]])
        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
        
        # Draw bounding boxes
        draw = ImageDraw.Draw(image)
        for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
            box = [round(i, 2) for i in box.tolist()]
            label_str = model.config.id2label[label.item()]
            draw.rectangle(box, outline="red", width=3)
            draw.text((box[0], box[1]), f"{label_str}: {score:.2f}", fill="red")
        
        # Save output image
        output_filename = f"output_{filename}"
        output_filepath = os.path.join(app.config['UPLOAD_FOLDER'], output_filename)
        image.save(output_filepath)
        logger.info(f"Processed image saved: {output_filename}")
        
        return render_template('results.html', 
                             original_image=url_for('static', filename=f'uploads/{filename}'),
                             processed_image=url_for('static', filename=f'uploads/{output_filename}'))
    
    except Exception as e:
        logger.error(f"Error processing file: {str(e)}")
        return render_template('index.html', error=f"Error processing file: {str(e)}")

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