Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import PIL.Image
+
+# Class names for prediction
+CLASS_NAMES = ['Alphida', 'Army worm', 'Bacterial blight', 'Healthy leaf', 'Leaf spot', 'Powdery Mildew']
+
+# Load the pre-trained model
+def load_model():
+    try:
+        model = tf.keras.models.load_model('cotton_disease_model.keras')
+        return model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None
+
+# Prepare the image for prediction
+def prepare_image(image):
+    # Resize the image
+    img = image.resize((180, 180))
+    # Convert to numpy array
+    img_array = np.array(img)
+    # Expand dimensions to create a batch
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# Prediction function
+def predict_disease(model, image):
+    # Prepare the image
+    processed_image = prepare_image(image)
+    
+    # Make prediction
+    predictions = model.predict(processed_image)
+    score = tf.nn.softmax(predictions[0])
+    
+    # Get the predicted class and confidence
+    predicted_class_index = np.argmax(score)
+    predicted_class = CLASS_NAMES[predicted_class_index]
+    confidence = 100 * np.max(score)
+    
+    return predicted_class, confidence
+
+# Detailed disease information
+DISEASE_INFO = {
+    "Alphida": {
+        "description": "Aphids are small insects that can damage cotton plants by sucking sap and spreading viruses.",
+        "impact": "Low to moderate crop damage",
+        "treatment": "Use insecticidal soaps, neem oil, or introduce natural predators like ladybugs"
+    },
+    "Army worm": {
+        "description": "Army worms can cause significant damage by consuming leaf tissue, potentially reducing crop yield.",
+        "impact": "High crop damage potential",
+        "treatment": "Apply appropriate insecticides, practice crop rotation, maintain field hygiene"
+    },
+    "Bacterial blight": {
+        "description": "A bacterial disease that causes lesions and wilting in cotton plants.",
+        "impact": "Moderate to severe crop damage",
+        "treatment": "Use copper-based bactericides, remove infected plants, practice crop rotation"
+    },
+    "Leaf spot": {
+        "description": "A fungal disease that creates spots on leaves, potentially affecting plant health and productivity.",
+        "impact": "Moderate crop damage",
+        "treatment": "Apply fungicides, ensure proper plant spacing, avoid overhead irrigation"
+    },
+    "Powdery Mildew": {
+        "description": "A fungal disease that appears as a white powdery substance on leaf surfaces.",
+        "impact": "Moderate crop damage",
+        "treatment": "Use sulfur-based fungicides, improve air circulation, avoid overhead watering"
+    },
+    "Healthy leaf": {
+        "description": "The cotton plant leaf is in good health with no visible diseases or pest damage.",
+        "impact": "No negative impact",
+        "treatment": "Continue regular plant care and monitoring"
+    }
+}
+
+# Main prediction interface
+def cotton_disease_prediction(input_image):
+    # Load the model (you might want to load this once globally)
+    model = load_model()
+    
+    if model is None:
+        return "Error: Model could not be loaded", None, None
+    
+    if input_image is None:
+        return "Please upload an image", None, None
+    
+    try:
+        # Convert to PIL Image if it's not already
+        if not isinstance(input_image, PIL.Image.Image):
+            input_image = PIL.Image.fromarray(input_image)
+        
+        # Predict disease
+        predicted_class, confidence = predict_disease(model, input_image)
+        
+        # Get detailed information
+        disease_details = DISEASE_INFO.get(predicted_class, {})
+        
+        # Format result
+        result_text = f"Predicted Disease: {predicted_class}\n"
+        result_text += f"Confidence: {confidence:.2f}%\n\n"
+        result_text += f"Description: {disease_details.get('description', 'No additional information')}\n"
+        result_text += f"Impact: {disease_details.get('impact', 'Not specified')}\n"
+        result_text += f"Treatment: {disease_details.get('treatment', 'Consult an agricultural expert')}"
+        
+        return result_text, predicted_class, confidence
+    
+    except Exception as e:
+        return f"Error in prediction: {str(e)}", None, None
+
+# Create Gradio Interface
+def create_gradio_interface():
+    # Define input and output components
+    image_input = gr.Image(type="pil", label="Upload Cotton Leaf Image")
+    text_output = gr.Textbox(label="Prediction Results")
+    disease_label = gr.Label(label="Detected Disease")
+    confidence_number = gr.Number(label="Confidence Score")
+
+    # Create the Gradio interface
+    demo = gr.Interface(
+        fn=cotton_disease_prediction,
+        inputs=image_input,
+        outputs=[text_output, disease_label, confidence_number],
+        title="Cotton Plant Disease Detector",
+        description="Upload a cotton plant leaf image to detect potential diseases or assess health status.",
+        theme="huggingface",
+        examples=[
+            ["example_healthy_leaf.jpg"],
+            ["example_diseased_leaf.jpg"]
+        ]
+    )
+    
+    return demo
+
+# Launch the app
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    demo.launch()