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import streamlit as st
from transformers import pipeline
from PIL import Image
import torch
st.set_page_config(page_title="Aide au diagnostic radiologique", layout="wide")
def load_models():
models = {
'Fracture': "kathleen/vit-base-fracture-detection",
'Pneumothorax': "nickmuchi/pneumothorax-detection-vit",
'Pneumonie': "nickmuchi/chest-xray-pneumonia-detection"
}
loaded_models = {}
for name, model_id in models.items():
loaded_models[name] = pipeline("image-classification", model=model_id)
return loaded_models
@st.cache_resource
def get_models():
return load_models()
def main():
st.title("Assistant de diagnostic radiologique")
models = get_models()
uploaded_file = st.file_uploader("Télécharger une image radiologique", type=["jpg", "jpeg", "png"])
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, caption="Image téléchargée", use_column_width=True)
col1, col2, col3 = st.columns(3)
with col1:
if st.button("Détecter Fracture"):
with st.spinner("Analyse en cours..."):
result = models['Fracture'](image)
st.write(f"Résultat: {result[0]['label']}")
st.write(f"Confiance: {result[0]['score']:.2%}")
with col2:
if st.button("Détecter Pneumothorax"):
with st.spinner("Analyse en cours..."):
result = models['Pneumothorax'](image)
st.write(f"Résultat: {result[0]['label']}")
st.write(f"Confiance: {result[0]['score']:.2%}")
with col3:
if st.button("Détecter Pneumonie"):
with st.spinner("Analyse en cours..."):
result = models['Pneumonie'](image)
st.write(f"Résultat: {result[0]['label']}")
st.write(f"Confiance: {result[0]['score']:.2%}")
if __name__ == "__main__":
main() |