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import streamlit as st |
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from ultralytics import YOLO |
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from PIL import Image |
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import io |
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st.set_page_config(layout="centered") |
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@st.cache_resource |
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def load_model(): |
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return YOLO("keremberke/yolov8m-chest-xray-classification") |
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def main(): |
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st.title("Analyse Radiographie Thoracique") |
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model = load_model() |
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uploaded_file = st.file_uploader("Télécharger une radiographie", type=["jpg", "jpeg", "png"]) |
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if uploaded_file: |
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image = Image.open(uploaded_file) |
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resized_image = image.resize((640, 640)) |
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st.image(resized_image, width=400) |
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if st.button("Analyser"): |
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results = model.predict(source=resized_image) |
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st.write(f"Résultat: {results[0].names[results[0].probs.argmax()]}") |
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st.write(f"Confiance: {results[0].probs.max():.2%}") |
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if __name__ == "__main__": |
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main() |
