Create app.py

app.py

@@ -0,0 +1,51 @@
+from ultralytics import YOLO
+from flask import Flask, request, jsonify
+from PIL import Image, ImageDraw
+import io
+
+app = Flask(__name__)
+model = YOLO('last.pt')  # Load your YOLO model
+class_names = ['Acne', 'Dark circles', 'blackheads', 'eczema', 'rosacea', 'whiteheads', 'wrinkles']
+
+@app.route('/classify', methods=['POST'])
+def classify_image():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image provided"}), 400
+
+    file = request.files['image']
+    if file.filename == '':
+        return jsonify({"error": "Empty image file"}), 400
+
+    image = Image.open(io.BytesIO(file.read()))
+    resized_image = image.copy()
+    resized_image.thumbnail((640, 640))
+
+    # Get results from the model
+    
+    results = model(resized_image)[0]
+    
+    predictions = []
+
+    if results.boxes is not None:
+        boxes = results.boxes.xyxy
+        confidences = results.boxes.conf
+        classes = results.boxes.cls
+
+        for i in range(len(boxes)):
+            box = boxes[i]
+            confidence = confidences[i].item()
+            class_id = int(classes[i].item())
+            prediction = {
+                "x1": box[0].item(),
+                "y1": box[1].item(),
+                "x2": box[2].item(),
+                "y2": box[3].item(),
+                "confidence": confidence,
+                "class": class_names[class_id],
+            }
+            predictions.append(prediction)
+
+    return jsonify({"predictions": predictions})
+
+if __name__ == '__main__':
+    app.run(host='127.0.0.1', port=5000, debug=True)