Delete app.py

app.py

@@ -1,52 +0,0 @@
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import tensorflow as tf
-from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input, decode_predictions
-from PIL import Image
-import numpy as np
-import io
-
-app = Flask(__name__)
-CORS(app)
-
-# Load pre-trained model (MobileNetV2 - lightweight for free tier)
-model = MobileNetV2(weights='imagenet')
-
-@app.route('/health', methods=['GET'])
-def health():
-    return jsonify({'status': 'healthy', 'model': 'MobileNetV2'})
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        if 'image' not in request.files:
-            return jsonify({'error': 'No image provided'}), 400
-        
-        file = request.files['image']
-        img = Image.open(io.BytesIO(file.read()))
-        
-        # Preprocess image
-        img = img.resize((224, 224))
-        img_array = np.array(img)
-        img_array = np.expand_dims(img_array, axis=0)
-        img_array = preprocess_input(img_array)
-        
-        # Make prediction
-        predictions = model.predict(img_array)
-        decoded = decode_predictions(predictions, top=5)[0]
-        
-        results = [
-            {'label': label, 'confidence': float(confidence)}
-            for (_, label, confidence) in decoded
-        ]
-        
-        return jsonify({
-            'success': True,
-            'predictions': results
-        })
-    
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860, debug=False)