Update app.py

app.py

@@ -1,53 +1,52 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from PIL import Image
-import matplotlib.pyplot as plt
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-from tensorflow.keras.applications.efficientnet import preprocess_input
-  
-def predict(uploaded_file, model, classes):
-    img = Image.open(uploaded_file)
-    img = img.resize((300, 300))
-    img_array = np.array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array = preprocess_input(img_array)
-
-    prediction = model.predict(img_array)
-    predicted_class_index = np.argmax(prediction)
-    predicted_class_label = classes[predicted_class_index]
-
-    st.write(f"Predicted Vehicle: {predicted_class_label}")
-    st.image(img, use_column_width=True)
-
-def run():
-    st.header('Vehicle Type Recognition 	:busstop:')
-    st.write('The objective of this project is to build a machine learning model to classify vehicles into the following categories using Convolutional Neural Networks.')
-    st.markdown("""
-                - Auto Rickshaw  :auto_rickshaw:
-                - Bicycle  :bicyclist:
-                - Bus  :bus:
-                - Car  :car:
-                - Motorcycle  :racing_motorcycle:
-                - Truck  :truck:
-                - Van 	:minibus:
-                """)
-
-    with st.form(key='Form Upload Vehicle Type Recognition'):
-        uploaded_files = st.file_uploader("Choose a .JPEG/.JPG/.PNG file", accept_multiple_files=True)
-
-        if uploaded_files:
-            for uploaded_file in uploaded_files:
-
-                st.write("filename:", uploaded_file.name)
-                model = load_model('vehicle_recognition_model.keras')
-                classes = ['Auto-rickshaw :auto_rickshaw:', 'Bicycle :bicyclist:', 'Bus :bus:', 'Car :car:', 'Motorcycle :racing_motorcycle:', 'Truck :truck:', 'Van 	:minibus:']
-                predict(uploaded_file, model, classes)
-
-        st.form_submit_button(label='Submit')
-
-if __name__ == '__main__':
+import streamlit as st
+import pandas as pd
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.efficientnet import preprocess_input
+  
+def predict(uploaded_file, model, classes):
+    img = Image.open(uploaded_file)
+    img = img.resize((300, 300))
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    prediction = model.predict(img_array)
+    predicted_class_index = np.argmax(prediction)
+    predicted_class_label = classes[predicted_class_index]
+
+    st.write(f"Predicted Vehicle: {predicted_class_label}")
+    st.image(img, use_column_width=True)
+
+def run():
+    st.header('Vehicle Type Recognition 	:busstop:')
+    st.write('The objective of this project is to build a machine learning model to classify vehicles into the following categories using Convolutional Neural Networks.')
+    st.markdown("""
+                - Auto Rickshaw  :auto_rickshaw:
+                - Bicycle  :bicyclist:
+                - Bus  :bus:
+                - Car  :car:
+                - Motorcycle  :racing_motorcycle:
+                - Truck  :truck:
+                - Van 	:minibus:
+                """)
+
+    with st.form(key='Form Upload Vehicle Type Recognition'):
+        uploaded_files = st.file_uploader("Choose a .JPEG/.JPG/.PNG file", accept_multiple_files=True)
+
+        if uploaded_files:
+            for uploaded_file in uploaded_files:
+
+                st.write("filename:", uploaded_file.name)
+                model = load_model('vehicle_recognition_model.keras')
+                classes = ['Auto-rickshaw :auto_rickshaw:', 'Bicycle :bicyclist:', 'Bus :bus:', 'Car :car:', 'Motorcycle :racing_motorcycle:', 'Truck :truck:', 'Van 	:minibus:']
+                predict(uploaded_file, model, classes)
+
+        st.form_submit_button(label='Submit')
+
+if __name__ == '__main__':
     run()