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import streamlit as st |
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from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline |
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from PIL import Image |
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import torch |
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st.set_page_config(page_title="Détection de fractures", layout="wide") |
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@st.cache_resource |
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def load_model(): |
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return pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray") |
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def main(): |
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st.title("Détection de fractures osseuses") |
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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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st.image(image, caption="Radiographie", use_column_width=True) |
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if st.button("Analyser"): |
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with st.spinner("Analyse en cours..."): |
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try: |
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result = model(image) |
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st.write(f"Résultat: {result[0]['label']}") |
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st.write(f"Confiance: {result[0]['score']:.2%}") |
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except Exception as e: |
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st.error(f"Erreur: {str(e)}") |
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if __name__ == "__main__": |
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main() |
