app.py

#import necessary libraries
import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from huggingface_hub import from_pretrained_keras
import numpy as np

def detect_cancer(img):
    #Load the model
    model = from_pretrained_keras('MUmairAB/Breast_Cancer_Detector')
    #Convert the NumPy image to tensor
    img = tf.convert_to_tensor(img)
    #Convert the single images to batch image
    img = tf.expand_dims(img, axis=0)
    #Make predictions
    pred = model.predict(img)
    #Convert the "numpy.ndarray" object to a simple numebr
    prediction = round(float(pred))
    if prediction == 0:
        return("Congratulation! you don't have breast cancer")
    else:
        return("Unfortunately! you have breast cancer. Kindly consult a doctor!")
  


gr.Interface(fn=detect_cancer, 
             inputs=gr.Image(shape=(50, 50)),
             outputs=gr.Label("The Test Result"),
             examples=[0, 1]).launch()