Update app.py

app.py

@@ -1,5 +1,5 @@
 #import necessary libraries
-import streamlit as st
+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
@@ -8,7 +8,7 @@ import numpy as np
 def detect_cancer(img):
     #Load the model
     model = from_pretrained_keras('MUmairAB/Breast_Cancer_Detector')
-    #Convert the PIL ImageFile to image tensor
+    #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)
@@ -20,35 +20,14 @@ def detect_cancer(img):
         return("Congratulation! you don't have breast cancer")
     else:
         return("Unfortunately! you have breast cancer. Kindly consult a doctor!")
-
-
-    
+  
 
 
 def main():
-    
-    st.title('Breast Cancer Detection App')
-    st.write("Created by: [Umair Akram](https://www.linkedin.com/in/m-umair01/)")
-   
-    h1 = "This App uses Deep Learning to predict whether you have Breast Cancer or not!"
-    st.subheader(h1)
-    st.write("The model is built using Convolutional Neural Network (CNN) in TensorFlow. Its code and other interesting projects are available on my [website](https://mumairab.github.io/)")
-
-    h2 = "Enter the following values to know the status of your health"
-    st.write(h2)
-    loaded_img = st.file_uploader(label="Upload the Histopathological Image patch of breast",
-                     type=None, accept_multiple_files=False,
-                     key="Img_upload_key", 
-                     label_visibility="visible")
-
-    if loaded_img is not None:
-        # Convert the file to TensorFlow image.
-        loaded_img = np.asarray(bytearray(loaded_img.read()), dtype=np.uint8)
-        img = load_img(loaded_img, target_size=(50,50))
-    
-    if st.button(label='Show the Test Results'):
-        prediction = detect_cancer(img)
-        st.success(prediction)
-    
+    gr.Interface(fn=detect_cancer, 
+             inputs=gr.Image(shape=(50, 50)),
+             outputs=gr.Label("The Test Result"),
+             examples=[0, 1]).launch()
+        
 if __name__ == '__main__':
     main()