Update app.py

app.py

@@ -1,43 +1,7 @@
-from flask import Flask, request, jsonify
 import tensorflow as tf
-import numpy as np
-from PIL import Image
-import io
-import json
 
-app = Flask(__name__)
+# Load the existing model
+model = tf.keras.models.load_model('./plant_disease_detection_saved_model')
 
-# Load the H5 model
-model = tf.keras.models.load_model('plant_disease_detection.h5')
-
-# Load categories
-with open('categories.json') as f:
-    categories = json.load(f)
-
-def preprocess_image(image):
-    # Convert the image to a NumPy array
-    image = Image.open(io.BytesIO(image))
-    image = image.resize((224, 224))  # Adjust size as needed
-    image_array = np.array(image) / 255.0  # Normalize to [0, 1]
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
-    return image_array
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    if 'image' not in request.files:
-        return jsonify({'error': 'No image provided'}), 400
-
-    image = request.files['image'].read()
-    image_array = preprocess_image(image)
-
-    # Make prediction
-    predictions = model.predict(image_array)
-    predicted_class = np.argmax(predictions, axis=1)[0]
-
-    # Map to category names
-    predicted_label = categories.get(str(predicted_class), 'Unknown')
-
-    return jsonify({'class': predicted_label, 'confidence': float(predictions[0][predicted_class])})
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=8080, debug=True)
+# Save it in the SavedModel format
+model.save('./plant_disease_detection_saved_model', save_format='tf')