Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from io import BytesIO
+
+# Load Model
+MODEL = tf.keras.models.load_model("new_cnn_model_tf.h5")
+CLASS_NAMES = ["Early Blight", "Late Blight", "Healthy"]
+
+# Function to process and predict
+def predict(image):
+    # Convert image to numpy array
+    image = np.array(image)
+    
+    # Expand dimensions to match model input shape
+    img_batch = np.expand_dims(image, 0)
+    
+    # Make prediction
+    predictions = MODEL.predict(img_batch)
+    
+    # Get class and confidence
+    predicted_class = CLASS_NAMES[np.argmax(predictions[0])]
+    confidence = float(np.max(predictions[0]))
+    
+    return f"Prediction: {predicted_class} (Confidence: {confidence:.2f})"
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=predict,               # Function to call
+    inputs=gr.Image(type="pil"),  # Image input (PIL format)
+    outputs="text",           # Text output (prediction result)
+    title="Potato Disease Classifier",
+    description="Upload an image of a potato leaf, and the model will predict whether it's Healthy, Early Blight, or Late Blight."
+)
+
+# Launch the app
+iface.launch()