Delete deploypalm.py

deploypalm.py

@@ -1,36 +0,0 @@
-import os
-import streamlit as st
-import cv2
-from PIL import Image
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.applications.resnet50 import preprocess_input 
-from tensorflow.keras.preprocessing.image import img_to_array    
-
-st.title('Palm Identification')
-st.markdown("This is a Deep Learning application to identify if a satellite image clip contains Palm trees.\n")
-st.markdown('The predicting result will be "Palm", or "Others".')
-st.markdown('You can click "Brows files" multiple times until adding all images before generating prediction.\n')
-
-uploaded_file = st.file_uploader("Upload an image file", type="jpg")
-st.image(uploaded_file, width=100)
-
-img_height = 224
-img_width = 224
-class_names = ['Palm', 'Others']
-
-model = tf.keras.models.load_model('model')
-
-if uploaded_file is not None:
-    Generate_pred = st.button("Generate Prediction")
-    if Generate_pred:
-        for file in uploaded_file:
-            img = Image.open(file)
-            img_array = img_to_array(img)
-            img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
-            processed_image = preprocess_input(img_array)
-    
-            predictions = model.predict(processed_image)
-            score = predictions[0]
-            st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
-