Upload 3 files

app.py

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+# METEHAN AYHAN
+
+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+
+model = tf.keras.models.load_model('model.h5')
+
+
+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:'Veh > 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 veh > 3.5 tons' }
+
+st.title('German Traffic Sign Recognition - Metehan Ayhan')
+st.write("Upload an image of a traffic sign to predict its class.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Traffic Sign.', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    image = image.resize((32, 32)) 
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Modelin beklediği şekil 
+    
+
+    predictions = model.predict(image)
+    predicted_class = np.argmax(predictions[0])
+
+  
+    st.write(f"Prediction: {classes[predicted_class]}")

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3abf6b949293ff203ce8a80bce4c693258c88f01dbfc7ed08fd9c346a4d38a01
+size 2405320

requirements.txt

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+streamlit
+Pillow
+tensorflow
+numpy