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import streamlit as st |
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from transformers import 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="Aide au diagnostic radiologique", layout="wide") |
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def load_models(): |
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models = { |
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'Fracture': "kathleen/vit-base-fracture-detection", |
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'Pneumothorax': "nickmuchi/pneumothorax-detection-vit", |
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'Pneumonie': "nickmuchi/chest-xray-pneumonia-detection" |
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} |
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loaded_models = {} |
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for name, model_id in models.items(): |
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loaded_models[name] = pipeline("image-classification", model=model_id) |
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return loaded_models |
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@st.cache_resource |
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def get_models(): |
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return load_models() |
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def main(): |
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st.title("Assistant de diagnostic radiologique") |
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models = get_models() |
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uploaded_file = st.file_uploader("Télécharger une image radiologique", 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="Image téléchargée", use_column_width=True) |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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if st.button("Détecter Fracture"): |
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with st.spinner("Analyse en cours..."): |
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result = models['Fracture'](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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with col2: |
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if st.button("Détecter Pneumothorax"): |
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with st.spinner("Analyse en cours..."): |
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result = models['Pneumothorax'](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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with col3: |
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if st.button("Détecter Pneumonie"): |
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with st.spinner("Analyse en cours..."): |
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result = models['Pneumonie'](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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if __name__ == "__main__": |
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main() |
