src/streamlit_app.py

import streamlit as st
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model

class_names = {
    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 for vehicles over 3.5 metric tons",
    11: "Right-of-way at the next intersection",
    12: "Priority road",
    13: "Yield",
    14: "Stop",
    15: "No vehicles",
    16: "Vehicles over 3.5 metric tons prohibited",
    17: "No entry",
    18: "General caution",
    19: "Dangerous curve to the left",
    20: "Dangerous curve to the 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 of all speed and 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 of no passing by vehicles over 3.5 metric tons"
}

model = load_model("src/capstone_model.h5")

st.title("German Traffic Sign Recognition")

uploaded_file = st.file_uploader("Bir trafik işareti yükle", type=["jpg", "png"])

if uploaded_file is not None:
    image = Image.open(uploaded_file).convert('RGB').resize((64, 64))
    st.image(image, caption="Yüklenen Görüntü", width=300)

    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    prediction = model.predict(img_array)

    predicted_class = np.argmax(prediction)
    confidence = np.max(prediction) * 100

    predicted_label = class_names.get(predicted_class, "Bilinmeyen Sınıf")
    st.success(f"🚦 Tahmin Edilen Trafik Levhası: **{predicted_label}**")
    st.info(f"📊 Güven Skoru (Confidence): **{confidence:.2f}%**")