Create app.py

app.py

@@ -0,0 +1,45 @@
+from flask import Flask, request, jsonify
+from transformers import pipeline
+from PIL import Image
+import io
+
+app = Flask(__name__)
+
+# Load model at startup
+print("Loading model...")
+classifier = pipeline("image-classification", model="Xenova/mobilenet_v2_1.0_224")
+print("Model loaded successfully!")
+
+@app.route('/')
+def home():
+    return "Food Image Classifier API is running!"
+
+@app.route('/classify', methods=['POST'])
+def classify_image():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file provided"}), 400
+
+    file = request.files['file']
+
+    try:
+        # Convert file to PIL image
+        image = Image.open(io.BytesIO(file.read()))
+
+        # Run classification
+        results = classifier(image)
+
+        # Get top result
+        if results:
+            prediction = {
+                "label": results[0]['label'],
+                "confidence": results[0]['score']
+            }
+            return jsonify(prediction)
+        else:
+            return jsonify({"error": "No results returned"}), 500
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0", port=7860, debug=True)