Update app.py

app.py

@@ -1,74 +1,77 @@
 import gradio as gr
-import tensorflow as tf
 import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
 from PIL import Image
 
-# Load your trained model
-model = tf.keras.models.load_model('Model.hdf5')
+# Load your model
+model = load_model("Model.hdf5")
 
-# Define the class names based on your image
-# I created this list from the dictionary you provided
-class_names = [
-    '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___Yellow_Leaf_Curl_Virus', 'Tomato___mosaic_virus', 'Tomato___healthy'
-]
+# Class mapping (from your screenshot)
+class_indices = {
+    'Apple___Apple_scab': 0,
+    'Apple___Black_rot': 1,
+    'Apple___Cedar_apple_rust': 2,
+    'Apple___healthy': 3,
+    'Blueberry___healthy': 4,
+    'Cherry_(including_sour)___Powdery_mildew': 5,
+    'Cherry_(including_sour)___healthy': 6,
+    'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot': 7,
+    'Corn_(maize)___Common_rust': 8,
+    'Corn_(maize)___Northern_Leaf_Blight': 9,
+    'Corn_(maize)___healthy': 10,
+    'Grape___Black_rot': 11,
+    'Grape___Esca_(Black_Measles)': 12,
+    'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)': 13,
+    'Grape___healthy': 14,
+    'Orange___Haunglongbing_(Citrus_greening)': 15,
+    'Peach___Bacterial_spot': 16,
+    'Peach___healthy': 17,
+    'Pepper,_bell___Bacterial_spot': 18,
+    'Pepper,_bell___healthy': 19,
+    'Potato___Early_blight': 20,
+    'Potato___Late_blight': 21,
+    'Potato___healthy': 22,
+    'Raspberry___healthy': 23,
+    'Soybean___healthy': 24,
+    'Squash___Powdery_mildew': 25,
+    'Strawberry___Leaf_scorch': 26,
+    'Strawberry___healthy': 27,
+    'Tomato___Bacterial_spot': 28,
+    'Tomato___Early_blight': 29,
+    'Tomato___Late_blight': 30,
+    'Tomato___Leaf_Mold': 31,
+    'Tomato___Septoria_leaf_spot': 32,
+    'Tomato___Spider_mites Two-spotted_spider_mite': 33,
+    'Tomato___Target_Spot': 34,
+    'Tomato___Tomato_Yellow_Leaf_Curl_Virus': 35,
+    'Tomato___Tomato_mosaic_virus': 36,
+    'Tomato___healthy': 37
+}
 
+# Reverse mapping
+idx_to_class = {v: k for k, v in class_indices.items()}
 
 # Prediction function
-def predict(image):
-    """
-    This function takes an image, preprocesses it, and returns the model's prediction.
-    """
-    # --- PREPROCESSING STEP ---
-    # The input 'image' from Gradio is a NumPy array.
-    # IMPORTANT: You might need to change the resize dimensions to match your model's input shape.
-    # Common shapes are (224, 224) or (256, 256). I'll use (256, 256) as a default.
-    img = Image.fromarray(image).resize((256, 256))
-    img_array = np.array(img)
-    
-    # Normalize the image (if you did this during training)
-    # Common normalization is to scale pixel values to [0, 1]
-    img_array = img_array / 255.0
-    
-    # Add a batch dimension
-    img_batch = np.expand_dims(img_array, axis=0)
+def predict(img):
+    img = img.resize((224, 224))  # adjust if your model uses different input size
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
 
-    # --- PREDICTION STEP ---
-    prediction = model.predict(img_batch)
-    
-    # Get the index of the highest probability
-    predicted_class_index = np.argmax(prediction[0])
-    
-    # Get the predicted class name
-    predicted_class_name = class_names[predicted_class_index]
-    
-    # Get the confidence score
-    confidence = float(np.max(prediction[0]))
-    
-    # Return a dictionary of labels and their confidences
-    return {predicted_class_name: confidence}
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions[0])
+    confidence = np.max(predictions[0]) * 100
 
-# --- GRADIO INTERFACE ---
-iface = gr.Interface(
+    return f"{idx_to_class[predicted_class]} ({confidence:.2f}% confidence)"
+
+# Gradio interface
+demo = gr.Interface(
     fn=predict,
-    inputs=gr.Image(label="Upload a photo of a crop leaf 🌿"),
-    outputs=gr.Label(num_top_classes=1, label="Prediction Result"),
+    inputs=gr.Image(type="pil", label="Upload a leaf image"),
+    outputs=gr.Textbox(label="Prediction"),
     title="Crop Disease Prediction",
-    description="Upload an image of a crop leaf to predict its disease. This model can identify 38 different conditions.",
-    examples=[
-        # You can add paths to example images here if you have any
-        # ["path/to/your/example1.jpg"],
-        # ["path/to/your/example2.jpg"]
-    ]
+    description="Upload an image of a crop leaf to predict its disease."
 )
 
-# Launch the app
-iface.launch()
+if __name__ == "__main__":
+    demo.launch()