import gradio as gr
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np

# Load the model
model = load_model('skin_cancer_model.h5')

# Mapping class index to class name
class_names = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel']

def predict_skin_cancer(img):
    # Preprocess the image
    img = img.resize((224, 224))
    img_array = np.array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    
    # Make prediction
    predictions = model.predict(img_array)
    predicted_class = np.argmax(predictions, axis=1)[0]
    
    # Return the class name
    return f"Predicted class: {class_names[predicted_class]}"

# Create a Gradio interface
interface = gr.Interface(
    fn=predict_skin_cancer,
    inputs=gr.Image(type="pil", label="Upload Skin Lesion Image"),
    outputs="text",
    title="Skin Cancer Prediction",
    description="Upload an image of a skin lesion to predict its class.",
)

# Launch the app
interface.launch()