app.py

import requests
import tensorflow as tf
import numpy as np
import cv2
import gradio as gr
from tensorflow.keras.models import load_model

response = requests.get("https://raw.githubusercontent.com/IuryChagas25/Chest_Cancer_Detection/main/Labels.txt")
labels = response.text.split("\n")

model = load_model("model2.h5") # load the model

def classify_image(image):
    new_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    new_image = new_image / 255.0
    resized_image = cv2.resize(new_image,(250,250))
    prediction = model.predict(np.array([resized_image])).flatten()
    return {labels[i]: float(prediction[i]) for i in range(4)}
    
    
image = gr.inputs.Image(shape=(250, 250))
label = gr.outputs.Label(num_top_classes=4)

demo = gr.Interface(
    fn=classify_image, inputs=image, outputs=label, examples=[["000110.png"], ["000111 (2).png"], ["6 - Copy.png"]], theme="huggingface", interpretation="shap", num_shap = 5)

if __name__ == "__main__":
    demo.launch(share='True')