Update app.py

app.py

@@ -1,10 +1,62 @@
-from flask import Flask, render_template
+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
 
 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
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
 @app.route('/')
-def home():
+def index():
     return render_template('index.html')
 
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'file' not in request.files:
+        return redirect(request.url)
+    file = request.files['file']
+    if file.filename == '':
+        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)
+    
+    return render_template('results.html', original_image=url_for('static', filename=f'uploads/{filename}'),
+                          processed_image=url_for('static', filename=f'uploads/{output_filename}'))
+
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=7860)