Add application file and requirements

app.py

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+import streamlit as st
+import numpy as np
+from keras.models import load_model
+from PIL import Image
+import tensorflow as tf
+import cv2
+
+
+classes = { 0:'Speed limit (20km/h)',
+            1:'Speed limit (30km/h)',
+            2:'Speed limit (50km/h)',
+            3:'Speed limit (60km/h)',
+            4:'Speed limit (70km/h)',
+            5:'Speed limit (80km/h)',
+            6:'End of speed limit (80km/h)',
+            7:'Speed limit (100km/h)',
+            8:'Speed limit (120km/h)',
+            9:'No passing',
+            10:'No passing veh over 3.5 tons',
+            11:'Right-of-way at intersection',
+            12:'Priority road',
+            13:'Yield',
+            14:'Stop',
+            15:'No vehicles',
+            16:'Vehicle > 3.5 tons prohibited',
+            17:'No entry',
+            18:'General caution',
+            19:'Dangerous curve left',
+            20:'Dangerous curve right',
+            21:'Double curve',
+            22:'Bumpy road',
+            23:'Slippery road',
+            24:'Road narrows on the right',
+            25:'Road work',
+            26:'Traffic signals',
+            27:'Pedestrians',
+            28:'Children crossing',
+            29:'Bicycles crossing',
+            30:'Beware of ice/snow',
+            31:'Wild animals crossing',
+            32:'End speed + passing limits',
+            33:'Turn right ahead',
+            34:'Turn left ahead',
+            35:'Ahead only',
+            36:'Go straight or right',
+            37:'Go straight or left',
+            38:'Keep right',
+            39:'Keep left',
+            40:'Roundabout mandatory',
+            41:'End of no passing',
+            42:'End no passing vehicle > 3.5 tons' }
+def image_processing(img):
+    model = load_model('TSR.h5')
+    image = Image.open(img)
+    image = image.resize((30,30))
+    image = np.expand_dims(image, axis=0)
+    image = np.array(image)
+    predict_x=model.predict(image)
+    classes_x=np.argmax(predict_x,axis=1)
+    sign = classes[int(classes_x)]
+    return sign
+
+st.title('Traffic Sign Recognition')
+st.write('This is a simple image classification web app to predict traffic signs.')
+st.write('Please upload an image file to classify.')
+file = st.file_uploader("Please upload an image file", type=["jpg", "png"])
+if file is None:
+    st.text("You haven't uploaded an image file")
+else:
+    image = Image.open(file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("")
+    label = image_processing(file)
+    st.success('This image most likely belongs to {} with a {:.2f} percent confidence.'.format(label, 100))
+    st.write('Done!')

requirements.txt

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+streamlit
+numpy
+keras
+tensorflow
+opencv-python
+