Update app.py

app.py

@@ -1,65 +1,33 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline
 from PIL import Image
 import torch
 
-st.set_page_config(page_title="Aide au diagnostic radiologique", layout="wide")
-
-def load_models():
-    models = {
-        'Fracture': pipeline("image-classification", model="stanfordaimi/fracture-detection-vit"),
-        'Pneumothorax': pipeline("image-classification", model="ahmedos/pneumothorax-detection"),
-        'Pneumonie': pipeline("image-classification", model="ahmedos/pneumonia-detection")
-    }
-    return models
+st.set_page_config(page_title="Détection de fractures", layout="wide")
 
 @st.cache_resource
-def get_models():
-    return load_models()
+def load_model():
+    return pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray")
 
 def main():
-    st.title("Assistant de diagnostic radiologique")
+    st.title("Détection de fractures osseuses")
     
-    with st.spinner("Chargement des modèles..."):
-        models = get_models()
+    model = load_model()
     
-    uploaded_file = st.file_uploader("Télécharger une image radiologique", type=["jpg", "jpeg", "png"])
+    uploaded_file = st.file_uploader("Télécharger une radiographie", 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..."):
-                    try:
-                        result = models['Fracture'](image)
-                        st.write(f"Résultat: {result[0]['label']}")
-                        st.write(f"Confiance: {result[0]['score']:.2%}")
-                    except Exception as e:
-                        st.error(f"Erreur: {str(e)}")
-        
-        with col2:
-            if st.button("Détecter Pneumothorax"):
-                with st.spinner("Analyse en cours..."):
-                    try:
-                        result = models['Pneumothorax'](image)
-                        st.write(f"Résultat: {result[0]['label']}")
-                        st.write(f"Confiance: {result[0]['score']:.2%}")
-                    except Exception as e:
-                        st.error(f"Erreur: {str(e)}")
+        st.image(image, caption="Radiographie", use_column_width=True)
         
-        with col3:
-            if st.button("Détecter Pneumonie"):
-                with st.spinner("Analyse en cours..."):
-                    try:
-                        result = models['Pneumonie'](image)
-                        st.write(f"Résultat: {result[0]['label']}")
-                        st.write(f"Confiance: {result[0]['score']:.2%}")
-                    except Exception as e:
-                        st.error(f"Erreur: {str(e)}")
+        if st.button("Analyser"):
+            with st.spinner("Analyse en cours..."):
+                try:
+                    result = model(image)
+                    st.write(f"Résultat: {result[0]['label']}")
+                    st.write(f"Confiance: {result[0]['score']:.2%}")
+                except Exception as e:
+                    st.error(f"Erreur: {str(e)}")
 
 if __name__ == "__main__":
     main()