Create app.py

app.py

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+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 TensorFlow model
+model = tf.keras.models.load_model('./plant_disease_detection_saved_model')
+
+# 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)