Update app.py

app.py

@@ -4,12 +4,11 @@ from PIL import Image, ImageDraw
 import torch
 
 st.set_page_config(
-    page_title="Fracture Detection",
+    page_title="Knochenbrucherkennung",
     layout="wide",
     initial_sidebar_state="collapsed"
 )
 
-# Custom CSS
 st.markdown("""
 <style>
     .main > div {
@@ -18,16 +17,6 @@ st.markdown("""
         border-radius: 1rem;
         box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
     }
-    .stButton button {
-        width: 100%;
-        border-radius: 0.5rem;
-    }
-    .uploadedFile {
-        border-radius: 0.5rem;
-    }
-    h1, h2, h3 {
-        color: #2c3e50;
-    }
 </style>
 """, unsafe_allow_html=True)
 
@@ -37,44 +26,54 @@ def load_models():
         "D3STRON": pipeline("object-detection", model="D3STRON/bone-fracture-detr"),
         "Heem2": pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray"),
         "Nandodeomkar": pipeline("image-classification", 
-            model="nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388"),
-        "Judy07": pipeline("object-detection", model="Judy07/bone-fracture-DETA")
+            model="nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388")
+    }
+
+def translate_label(label):
+    translations = {
+        "fracture": "Knochenbruch",
+        "no fracture": "Kein Bruch",
+        "normal": "Normal",
+        "abnormal": "Abnormal"
     }
+    for eng, deu in translations.items():
+        if eng.lower() in label.lower():
+            return deu
+    return label
 
-def draw_boxes(image, predictions, color):
+def draw_boxes(image, predictions):
     draw = ImageDraw.Draw(image)
     for pred in predictions:
         box = pred['box']
-        label = f"{pred['label']} ({pred['score']:.2%})"
+        label = f"{translate_label(pred['label'])} ({pred['score']:.2%})"
         
         draw.rectangle(
             [(box['xmin'], box['ymin']), (box['xmax'], box['ymax'])],
-            outline=color,
+            outline="#FF6B6B",
             width=2
         )
         
-        # Label background
         text_bbox = draw.textbbox((box['xmin'], box['ymin']), label)
-        draw.rectangle(text_bbox, fill=color)
+        draw.rectangle(text_bbox, fill="#FF6B6B")
         draw.text((box['xmin'], box['ymin']), label, fill="white")
     return image
 
 def main():
-    st.title("🦴 Advanced Fracture Detection System")
+    st.title("🦴 Knochenbrucherkennung System")
     
     models = load_models()
     
-    with st.expander("⚙️ Settings", expanded=True):
+    with st.expander("⚙️ Einstellungen", expanded=True):
         conf_threshold = st.slider(
-            "Confidence threshold",
+            "Konfidenzschwelle",
             min_value=0.0,
             max_value=1.0,
-            value=0.3,
+            value=0.60,
             step=0.01
         )
     
     uploaded_file = st.file_uploader(
-        "Upload X-ray image",
+        "Röntgenbild hochladen",
         type=['png', 'jpg', 'jpeg'],
         key="xray_upload"
     )
@@ -84,18 +83,18 @@ def main():
         
         with col1:
             image = Image.open(uploaded_file)
-            max_size = (300, 300)
+            max_size = (250, 250)
             image.thumbnail(max_size, Image.Resampling.LANCZOS)
-            st.image(image, caption="Original X-ray", use_column_width=True)
+            st.image(image, caption="Original Röntgenbild", use_container_width=True)
 
         with col2:
-            tab1, tab2 = st.tabs(["📊 Classifications", "🔍 Detections"])
+            tab1, tab2 = st.tabs(["📊 Klassifizierung", "🔍 Erkennung"])
             
             with tab1:
                 for name in ["Heem2", "Nandodeomkar"]:
                     with st.container():
-                        st.subheader(f"Model: {name}")
-                        with st.spinner("Analyzing..."):
+                        st.subheader(f"Modell: {name}")
+                        with st.spinner("Analyse läuft..."):
                             predictions = models[name](image)
                             for pred in predictions:
                                 if pred['score'] >= conf_threshold:
@@ -104,34 +103,30 @@ def main():
                                         <div style='padding: 10px; border-radius: 5px; background-color: #f0f2f6;'>
                                             <span style='color: {score_color}; font-weight: bold;'>
                                                 {pred['score']:.1%}
-                                            </span> - {pred['label']}
+                                            </span> - {translate_label(pred['label'])}
                                         </div>
                                     """, unsafe_allow_html=True)
 
             with tab2:
-                detection_colors = {"D3STRON": "#FF6B6B", "Judy07": "#4ECDC4"}
-                
-                for name, color in detection_colors.items():
-                    with st.container():
-                        st.subheader(f"Model: {name}")
-                        with st.spinner("Detecting..."):
-                            predictions = models[name](image)
-                            filtered_preds = [p for p in predictions if p['score'] >= conf_threshold]
-                            
-                            if filtered_preds:
-                                result_image = image.copy()
-                                result_image = draw_boxes(result_image, filtered_preds, color)
-                                st.image(result_image, use_column_width=True)
-                                
-                                for pred in filtered_preds:
-                                    st.markdown(f"""
-                                        <div style='padding: 8px; border-left: 4px solid {color}; 
-                                             margin: 5px 0; background-color: #f0f2f6;'>
-                                            {pred['label']}: {pred['score']:.1%}
-                                        </div>
-                                    """, unsafe_allow_html=True)
-                            else:
-                                st.info("No detections above threshold")
+                st.subheader("Modell: D3STRON")
+                with st.spinner("Erkennung läuft..."):
+                    predictions = models["D3STRON"](image)
+                    filtered_preds = [p for p in predictions if p['score'] >= conf_threshold]
+                    
+                    if filtered_preds:
+                        result_image = image.copy()
+                        result_image = draw_boxes(result_image, filtered_preds)
+                        st.image(result_image, use_container_width=True)
+                        
+                        for pred in filtered_preds:
+                            st.markdown(f"""
+                                <div style='padding: 8px; border-left: 4px solid #FF6B6B; 
+                                     margin: 5px 0; background-color: #f0f2f6;'>
+                                    {translate_label(pred['label'])}: {pred['score']:.1%}
+                                </div>
+                            """, unsafe_allow_html=True)
+                    else:
+                        st.info("Keine Erkennungen über dem Schwellenwert")
 
 if __name__ == "__main__":
     main()