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import streamlit as st |
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import numpy as np |
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from PIL import Image |
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import base64 |
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import io |
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from tensorflow.keras.models import load_model |
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st.set_page_config(page_title="Hurma Sınıflandırıcı", layout="centered") |
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st.title("📷 Hurma Resmi Sınıflandırma") |
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st.write("Lütfen 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(f"Model yüklenemedi: {e}") |
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st.stop() |
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class_names = [ |
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'Rutab', 'Meneifi', 'Sokari', 'Galaxy', 'Shaishe', |
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'Medjool', 'Ajwa', 'Nabtat Ali', 'Sugaey' |
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] |
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def image_to_base64(image_bytes): |
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return base64.b64encode(image_bytes).decode("utf-8") |
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def base64_to_image(base64_str): |
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return Image.open(io.BytesIO(base64.b64decode(base64_str))).convert("RGB") |
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def process_image(img): |
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img = img.resize((224, 224)) |
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img = np.array(img) / 255.0 |
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img = np.expand_dims(img, axis=0) |
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return img |
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if "image_data" not in st.session_state: |
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st.session_state.image_data = None |
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uploaded_file = st.file_uploader("Resim Seç (.jpg, .jpeg, .png)", type=["jpg", "jpeg", "png"]) |
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if uploaded_file is not None: |
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st.session_state.image_data = image_to_base64(uploaded_file.read()) |
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if st.session_state.image_data: |
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try: |
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img = base64_to_image(st.session_state.image_data) |
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st.image(img, caption="Yüklenen Resim", use_column_width=True) |
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processed_img = process_image(img) |
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prediction = model.predict(processed_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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except Exception as e: |
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st.error(f"Fotoğraf işlenemedi: {e}") |
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