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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.Textbox(label="Patient's Name")
age_input = gr.inputs.Textbox(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()
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