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!")
  
#Define Gradio input components for reading image
input_img = gr.Image(shape=(50, 50))
#Define Gradio output component
output = 'text'

#Create a Gradio user interface
interfac = gr.Interface(title="Breast Cancer Diagnosis\n(by Umair Akram)", 
             description="Enter the Histopathological image of the breast to predict the diagnosis.",
             fn=detect_cancer, 
             inputs=input_img,
             outputs=output)

#Define the main function
if __name__ == "__main__":
    #Launch the Gradio interface
    interfac.launch()