Update app.py

app.py

@@ -4,6 +4,11 @@ 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__)
 
@@ -13,8 +18,10 @@ 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():
@@ -23,40 +30,51 @@ def index():
 @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)
     
-    # Save the uploaded file
-    filename = str(uuid.uuid4()) + os.path.splitext(file.filename)[1]
-    filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-    file.save(filepath)
-    
-    # Process image for object detection
-    image = Image.open(filepath).convert("RGB")
-    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)
+    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}'))
     
-    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)