Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+
+# Load model from Hugging Face model repository
+model = load_model("https://huggingface.co/syaha/skin_cancer_detection_model/resolve/main/skin_cancer_detection_model.h5")
+
+# Preprocess function
+def preprocess_image(image):
+    image = image.resize((224, 224))  # Resize to match model input size
+    image = np.array(image) / 255.0  # Normalize
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Predict function
+def predict_image(image):
+    img = preprocess_image(image)
+    prediction = model.predict(img)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+    
+    class_label = disease_info[predicted_class]['name']
+    description = disease_info[predicted_class]['description']
+    
+    return f"Prediction: {class_label}\nDescription: {description}"
+
+# Disease information mapping
+disease_info = {
+    0: {'name': 'Actinic Keratoses (akiec)', 'description': 'Rough, scaly patches caused by sun exposure.'},
+    1: {'name': 'Basal Cell Carcinoma (bcc)', 'description': 'A type of skin cancer that rarely spreads.'},
+    2: {'name': 'Benign Keratosis (bkl)', 'description': 'Non-cancerous skin lesions.'},
+    3: {'name': 'Dermatofibroma (df)', 'description': 'A benign lesion often on the legs.'},
+    4: {'name': 'Melanocytic Nevus (nv)', 'description': 'Common mole, can develop into melanoma.'},
+    5: {'name': 'Vascular Lesions (vasc)', 'description': 'Blood vessel-related skin growths.'},
+    6: {'name': 'Melanoma (mel)', 'description': 'Most dangerous skin cancer, early detection is key.'}
+}
+
+# Gradio interface
+iface = gr.Interface(fn=predict_image, inputs="image", outputs="text")
+iface.launch()