Update app.py

app.py

@@ -1,6 +1,25 @@
+#import necessary libraries
 import streamlit as st
-import numpy as input
+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 PIL ImageFile to image 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))
+    return prediction
+
+
+    
 
 
 def main():
@@ -21,13 +40,8 @@ def main():
 
     if uploaded_file is not None:
     # Convert the file to TensorFlow image.
-    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-    img = load_img(file_bytes, target_size=(50,50))
-    #Rescale the image from [0,255] to [0,1]
-    np_image = np.array(image).astype('float32')/255.0
+    loaded_img = np.asarray(bytearray(loaded_img.read()), dtype=np.uint8)
+    img = load_img(loaded_img, target_size=(50,50))
+    prediction = detect_cancer(img)
     
-    image = tf.keras.utils.load_img(image_path)
-    input_arr = tf.keras.utils.img_to_array(image)
-    input_arr = np.array([input_arr])  # Convert single image to a batch.
-    predictions = model.predict(input_arr)