app.py

import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from PIL import Image

# Load the trained model
model = load_model('skin_model.h5')

# Define a function to make predictions
def predict(name, age, image):
    # Preprocess the image
    image = Image.fromarray(image)
    image = image.resize((150, 150))
    image = np.array(image) / 255.0
    image = np.expand_dims(image, axis=0)
    
    # Make a prediction using the model
    prediction = model.predict(image)
    
    # Get the predicted class label
    if prediction[0][0] < 0.5:
        label = 'Benign'
    else:
        label = 'Malignant'
    
    return f"Patient: {name}, Age: {age}, Prediction: {label}"

# Define input and output components
name_input = gr.inputs.Text(label="Patient's Name")
age_input = gr.inputs.Text(label="Patient's Age")
image_input = gr.inputs.Image(shape=(150, 150))
label_output = gr.outputs.Label()

# Define a Gradio interface for user interaction
iface = gr.Interface(
    fn=predict,
    inputs=[name_input, age_input, image_input],
    outputs=label_output,
    title="Skin Cancer Classification Chatbot",
    description="Predicts whether a skin image is cancerous or not based on patient's name, age, and lesion image.",
    theme="default",  # Choose a theme: "default", "compact", "huggingface"
    layout="vertical",  # Choose a layout: "vertical", "horizontal", "double"
    live=False  # Set to True for live updates without clicking "Submit"
)

iface.launch()