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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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@st.cache_resource |
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def load_model(): |
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return pipeline("image-classification", model="mrm8488/vit-base-patch16-224_finetuned-pneumothorax", top_k=2) |
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def main(): |
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st.title("Détection de Pneumothorax") |
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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).convert('RGB') |
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resized_image = image.resize((224, 224)) |
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col1, col2 = st.columns(2) |
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with col1: |
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st.image(resized_image, width=300, caption="Image originale") |
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if st.button("Analyser"): |
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with st.spinner("Analyse en cours..."): |
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results = model(resized_image) |
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with col2: |
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st.write("Résultats:") |
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for result in results: |
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st.write(f"{result['label']}: {result['score']:.2%}") |
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if __name__ == "__main__": |
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main() |
