Create app.py

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+import numpy as input
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+
+
+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=color["Upload the Histopathological Image patch of breast"],
+                     type=None, accept_multiple_files=False,
+                     key="Img_upload_key", 
+                     label_visibility="visible")
+
+    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
+    
+    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)
+