Update app.py

app.py

@@ -1,98 +1,56 @@
 import streamlit as st
 from transformers import pipeline
-import torch
-from PIL import Image, ImageDraw
+from PIL import Image
 import io
 
-st.set_page_config(page_title="Détection de Fractures Osseuses", layout="wide")
+st.set_page_config(page_title="Knochenbrucherkennung", layout="centered")
 
 @st.cache_resource
 def load_model():
-    return pipeline("object-detection", model="D3STRON/bone-fracture-detr")
-
-def draw_boxes(image, predictions):
-    draw = ImageDraw.Draw(image)
-    for pred in predictions:
-        box = pred['box']
-        label = f"{pred['label']} ({pred['score']:.2%})"
-        
-        # Draw bounding box
-        draw.rectangle(
-            [(box['xmin'], box['ymin']), (box['xmax'], box['ymax'])],
-            outline="red",
-            width=3
-        )
-        
-        # Draw label background
-        text_bbox = draw.textbbox((box['xmin'], box['ymin']), label)
-        draw.rectangle(text_bbox, fill="red")
-        
-        # Draw label text
-        draw.text(
-            (box['xmin'], box['ymin']),
-            label,
-            fill="white"
-        )
-    return image
+    return pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray")
 
 def main():
-    st.title("🦴 Détecteur de Fractures Osseuses")
-    st.write("Téléchargez une radiographie pour détecter les fractures osseuses.")
+    st.title("🦴 Knochenbrucherkennung")
+    st.write("Laden Sie ein Röntgenbild hoch.")
 
     pipe = load_model()
     
     uploaded_file = st.file_uploader(
-        "Choisissez une image de radiographie", 
+        "Röntgenbild auswählen", 
         type=['png', 'jpg', 'jpeg']
     )
 
     conf_threshold = st.slider(
-        "Seuil de confiance",
+        "Konfidenzschwelle",
         min_value=0.0,
         max_value=1.0,
-        value=0.5,
-        step=0.05
+        value=0.3,
+        step=0.01
     )
 
     if uploaded_file:
-        col1, col2 = st.columns(2)
-        
-        # Original image
         image = Image.open(uploaded_file)
-        col1.header("Image originale")
-        col1.image(image)
+        
+        # Redimensionner l'image
+        max_size = (400, 400)
+        image.thumbnail(max_size, Image.Resampling.LANCZOS)
+        
+        st.image(image, caption="Hochgeladenes Bild")
 
-        # Process image
-        with st.spinner("Analyse en cours..."):
+        with st.spinner("Analyse läuft..."):
             predictions = pipe(image)
             
-            # Filter predictions based on confidence threshold
-            filtered_preds = [
-                pred for pred in predictions 
-                if pred['score'] >= conf_threshold
-            ]
-            
-            # Draw boxes on a copy of the image
-            result_image = image.copy()
-            result_image = draw_boxes(result_image, filtered_preds)
-            
-            # Display results
-            col2.header("Résultats de la détection")
-            col2.image(result_image)
-            
-            # Display detailed predictions
-            if filtered_preds:
-                st.subheader("Détails des détections")
-                for pred in filtered_preds:
-                    st.write(
-                        f"• Type: {pred['label']} - "
-                        f"Confiance: {pred['score']:.2%}"
-                    )
-            else:
-                st.warning(
-                    "Aucune fracture détectée avec le seuil de confiance actuel. "
-                    "Essayez de baisser le seuil pour plus de résultats."
-                )
+            st.subheader("Ergebnisse")
+            for pred in predictions:
+                if pred['score'] >= conf_threshold:
+                    label = "Bruch erkannt" if "fracture" in pred['label'].lower() else "Kein Bruch"
+                    st.write(f"• Diagnose: {label}")
+                    st.write(f"• Konfidenz: {pred['score']:.2%}")
+                    
+                    if "fracture" in pred['label'].lower() and pred['score'] >= conf_threshold:
+                        st.warning("⚠️ Möglicher Knochenbruch erkannt!")
+                    else:
+                        st.success("✅ Kein Bruch erkannt")
 
 if __name__ == "__main__":
     main()