Update app.py

app.py

@@ -1,7 +1,5 @@
 from flask import Flask, render_template, request, redirect, url_for
-from transformers import DetrImageProcessor, DetrForObjectDetection
-from PIL import Image, ImageDraw
-import torch
+from PIL import Image
 import os
 import uuid
 import logging
@@ -17,12 +15,6 @@ 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')
@@ -44,33 +36,14 @@ def upload_file():
         file.save(filepath)
         logger.info(f"File saved: {filename}")
 
-        # Process image
+        # Verify and resize 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}")
-        
+        image = image.resize((800, 600))  # Resize for consistent display
+        image.save(filepath)  # Overwrite with resized image
+        logger.info(f"Image resized and saved: {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}'))
+                             image=url_for('static', filename=f'uploads/{filename}'))
     
     except Exception as e:
         logger.error(f"Error processing file: {str(e)}")