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import streamlit as st |
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from PIL import Image |
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import numpy as np |
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from tensorflow.keras.models import load_model |
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import io |
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def main(): |
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st.set_page_config(page_title="Hurma Sınıflandırıcı") |
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st.title("📷 Hurma Resmi Sınıflandırma") |
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st.write("Bir hurma resmi yükleyin ve hangi tür olduğunu tahmin edelim.") |
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try: |
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model = load_model("src/dates_classifier_model.h5") |
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except Exception as e: |
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st.error("❌ Model yüklenemedi.") |
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st.stop() |
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class_names = [ |
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'Rutab', |
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'Meneifi', |
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'Sokari', |
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'Galaxy', |
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'Shaishe', |
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'Medjool', |
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'Ajwa', |
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'Nabtat Ali', |
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'Sugaey' |
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] |
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file = st.file_uploader("Resim seç", type=["jpg", "jpeg", "png"]) |
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if file: |
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try: |
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image = Image.open(io.BytesIO(file.read())).convert("RGB") |
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st.image(image, caption="Yüklenen Resim", use_container_width=True) |
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img = image.resize((224, 224)) |
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img = np.array(img) |
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img = img / 255.0 |
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img = np.expand_dims(img, axis=0) |
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prediction = model.predict(img) |
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predicted_class = np.argmax(prediction) |
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st.success(f"Tahmin: {class_names[predicted_class]}") |
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st.subheader("Tahmin Skorları (Softmax Çıkışı):") |
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for i, score in enumerate(prediction[0]): |
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st.write(f"{class_names[i]}: {score:.4f}") |
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except Exception as e: |
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st.error(f"Hata: {str(e)}") |
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if __name__ == "__main__": |
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main() |
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