Add model and app

app.py

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+from PIL import Image
+import requests
+from io import BytesIO
+
+# Load your model (you'll need to upload trained_modela.keras to your space)
+model = tf.keras.models.load_model('trained_modela.keras')
+
+class_name = ['Apple___Apple_scab',
+    'Apple___Black_rot',
+    'Apple___Cedar_apple_rust',
+    'Apple___healthy',
+    'Blueberry___healthy',
+    'Cherry_(including_sour)___Powdery_mildew',
+    'Cherry_(including_sour)___healthy',
+    'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',
+    'Corn_(maize)___Common_rust_',
+    'Corn_(maize)___Northern_Leaf_Blight',
+    'Corn_(maize)___healthy',
+    'Grape___Black_rot',
+    'Grape___Esca_(Black_Measles)',
+    'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
+    'Grape___healthy',
+    'Orange___Haunglongbing_(Citrus_greening)',
+    'Peach___Bacterial_spot',
+    'Peach___healthy',
+    'Pepper,_bell___Bacterial_spot',
+    'Pepper,_bell___healthy',
+    'Potato___Early_blight',
+    'Potato___Late_blight',
+    'Potato___healthy',
+    'Raspberry___healthy',
+    'Soybean___healthy',
+    'Squash___Powdery_mildew',
+    'Strawberry___Leaf_scorch',
+    'Strawberry___healthy',
+    'Tomato___Bacterial_spot',
+    'Tomato___Early_blight',
+    'Tomato___Late_blight',
+    'Tomato___Leaf_Mold',
+    'Tomato___Septoria_leaf_spot',
+    'Tomato___Spider_mites Two-spotted_spider_mite',
+    'Tomato___Target_Spot',
+    'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
+    'Tomato___Tomato_mosaic_virus',
+    'Tomato___healthy']
+
+def predict_disease(image):
+    """
+    Predict plant disease from uploaded image
+    """
+    try:
+        # Preprocess the image
+        image = image.resize((128, 128))
+        input_arr = tf.keras.preprocessing.image.img_to_array(image)
+        input_arr = np.array([input_arr])  # Convert single image to a batch
+        input_arr = input_arr / 255.0  # Normalize if your model expects it
+        
+        # Make prediction
+        prediction = model.predict(input_arr)
+        result_index = np.argmax(prediction)
+        confidence = prediction[0][result_index]
+        
+        # Get disease name
+        disease_name = class_name[result_index]
+        
+        return f"Disease: {disease_name}\nConfidence: {confidence:.2%}"
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Image(type="pil", label="Upload Plant Image"),
+    outputs=gr.Textbox(label="Prediction Result"),
+    title="Plant Disease Detection API",
+    description="Upload an image of a plant leaf to detect diseases",
+    examples=[
+        # You can add example images here
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+tensorFlow: 2.19.0
+numpy: 2.2.6
+gradio: 5.44.1
+pillow: 10.4.0