Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load the trained model
+model = load_model("best_model.keras")
+
+# Define disease labels
+disease_labels = [
+    "Cellulitis", "Impetigo", "Athlete's Foot", "Nail Fungus", 
+    "Ringworm", "Cutaneous Larva Migrans", "Chickenpox", "Shingles"
+]
+
+# Define the prediction function
+def predict_disease(img):
+    # Preprocess the image
+    img = img.resize((224, 224))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0  # Normalize to [0, 1] range
+
+    # Make prediction
+    predictions = model.predict(img_array)
+    predicted_index = np.argmax(predictions)
+    
+    # Return the predicted disease
+    return disease_labels[predicted_index]
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs="text",
+    title="Skin Disease Classification",
+    description="Upload an image of skin disease to classify it."
+)
+
+# Launch the interface
+iface.launch()
+