import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image


st.title("Cat vs Dog Image Classification")

st.write("Bu uygulama yüklenen görselin kedi mi yoksa köpek mi olduğunu tahmin eder.")


model = tf.keras.models.load_model("src/cat_dog_cnn_model.h5")


uploaded_file = st.file_uploader(
    "Bir kedi veya köpek görseli yükleyin",
    type=["jpg", "jpeg", "png"]
)


if uploaded_file is not None:
    
    image = Image.open(uploaded_file).convert("RGB")
    
    st.image(image, caption="Yüklenen Görsel", use_container_width=True)
    
    image = image.resize((128, 128))
    
    img_array = np.array(image)
    
    img_array = np.expand_dims(img_array, axis=0)
    
    prediction = model.predict(img_array)
    
    if prediction[0][0] > 0.5:
        result = "Dog"
    else:
        result = "Cat"
    
    st.subheader("Tahmin Sonucu")
    st.write(result)
    
    score = float(prediction[0][0])

    if result == "Dog":
        confidence = score * 100
    else:
        confidence = (1 - score) * 100

    st.write(f"Güven Oranı: %{confidence:.2f}")