Update app.py

app.py

@@ -11,85 +11,61 @@ st.set_page_config(
 
 st.markdown("""
 <style>
-    /* Reset et base */
     .stApp {
-        background-color: var(--background-color) !important;
+        background-color: transparent !important;
         padding: 0 !important;
-        overflow: hidden !important;
     }
     
-    /* Variables de thème */
     [data-theme="light"] {
         --background-color: #ffffff;
         --text-color: #1f2937;
         --border-color: #e5e7eb;
-        --secondary-bg: #f3f4f6;
     }
     
     [data-theme="dark"] {
         --background-color: #1f2937;
         --text-color: #f3f4f6;
         --border-color: #4b5563;
-        --secondary-bg: #374151;
     }
     
-    /* Layout principal */
     .block-container {
         padding: 0.5rem !important;
         max-width: 100% !important;
     }
     
-    /* Contrôles et upload */
-    .uploadedFile {
-        border: 1px dashed var(--border-color);
-        border-radius: 0.375rem;
-        padding: 0.25rem;
-        background: var(--secondary-bg);
-    }
-    
-    /* Ajustement des colonnes */
-    [data-testid="column"] {
-        padding: 0 0.5rem !important;
-    }
-    
-    /* Images adaptatives */
     .stImage > img {
-        width: 100% !important;
-        height: auto !important;
-        max-height: 400px !important;
-        object-fit: contain !important;
+        max-height: 250px !important;
+        width: auto !important;
+        margin: 0 auto !important;
     }
     
-    /* Résultats */
     .result-box {
         padding: 0.375rem;
         border-radius: 0.375rem;
         margin: 0.25rem 0;
-        background: var(--secondary-bg);
+        background: var(--background-color);
         border: 1px solid var(--border-color);
         color: var(--text-color);
     }
     
-    /* Titres */
-    h2, h3 {
-        margin: 0 !important;
-        padding: 0.5rem 0 !important;
-        font-size: 1rem !important;
+    h2, h3, h4 {
+        margin: 0.5rem 0 !important;
         color: var(--text-color) !important;
+        font-size: 1rem !important;
     }
     
-    /* Nettoyage des éléments inutiles */
-    #MainMenu, footer, header, .viewerBadge_container__1QSob, .stDeployButton {
+    #MainMenu, footer, header {
         display: none !important;
     }
     
-    /* Ajustements espacement */
-    div[data-testid="stVerticalBlock"] {
-        gap: 0.5rem !important;
+    .uploadedFile {
+        border: 1px dashed var(--border-color);
+        border-radius: 0.375rem;
+        padding: 0.25rem;
     }
     
-    .element-container {
-        margin: 0.25rem 0 !important;
+    .row-widget.stButton {
+        text-align: center;
     }
 </style>
 """, unsafe_allow_html=True)
@@ -99,121 +75,62 @@ def load_models():
     return {
         "KnochenAuge": pipeline("object-detection", model="D3STRON/bone-fracture-detr"),
         "KnochenWächter": pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray"),
-        "RöntgenMeister": pipeline("image-classification", 
-            model="nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388")
+        "RöntgenMeister": pipeline("image-classification", 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": "Auffällig"
-    }
-    return translations.get(label.lower(), label)
-
 def draw_boxes(image, predictions):
     draw = ImageDraw.Draw(image)
     for pred in predictions:
-        box = pred['box']
-        label = f"{translate_label(pred['label'])} ({pred['score']:.2%})"
-        color = "#2563eb" if pred['score'] > 0.7 else "#eab308"
-        
-        draw.rectangle(
-            [(box['xmin'], box['ymin']), (box['xmax'], box['ymax'])],
-            outline=color,
-            width=2
-        )
-        
-        # Label plus compact
-        text_bbox = draw.textbbox((box['xmin'], box['ymin']-15), label)
-        draw.rectangle(text_bbox, fill=color)
-        draw.text((box['xmin'], box['ymin']-15), label, fill="white")
+        if pred['label'].lower() == 'fracture' and pred['score'] > 0.6:
+            box = pred['box']
+            label = f"Fraktur ({pred['score']:.2%})"
+            color = "#2563eb"
+            
+            draw.rectangle(
+                [(box['xmin'], box['ymin']), (box['xmax'], box['ymax'])],
+                outline=color,
+                width=2
+            )
+            
+            text_bbox = draw.textbbox((box['xmin'], box['ymin']-15), label)
+            draw.rectangle(text_bbox, fill=color)
+            draw.text((box['xmin'], box['ymin']-15), label, fill="white")
     return image
 
 def main():
     models = load_models()
-
-    # Disposition en deux colonnes principales
-    col1, col2 = st.columns([1, 2])
-
-    with col1:
-        st.markdown("### 📤 Röntgenbild Upload")
-        uploaded_file = st.file_uploader("", type=['png', 'jpg', 'jpeg'])
+    
+    st.markdown("### 📤 Röntgenbilder Upload")
+    uploaded_files = st.file_uploader("", type=['png', 'jpg', 'jpeg'], accept_multiple_files=True)
+    
+    if uploaded_files:
+        col1, col2 = st.columns([1, 1])
         
-        if uploaded_file:
-            conf_threshold = st.slider(
-                "Konfidenzschwelle",
-                min_value=0.0, max_value=1.0,
-                value=0.60, step=0.05
-            )
-
-    with col2:
-        if uploaded_file:
+        for idx, uploaded_file in enumerate(uploaded_files):
             image = Image.open(uploaded_file)
             
-            st.markdown("### 🔍 Meinung der KI-Experten")
-            
             # Analyse avec KnochenAuge (localisierung)
-            st.markdown("#### 👁️ Das KnochenAuge - Lokalisation")
             predictions = models["KnochenAuge"](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)
+            fractures_found = any(p['label'].lower() == 'fracture' and p['score'] > 0.6 for p in predictions)
             
-            # Autres modèles
-            st.markdown("#### 🎯 KI-Analyse")
-            col_left, col_right = st.columns(2)
-            
-            with col_left:
-                st.markdown("**🛡️ Der KnochenWächter**")
-                predictions = models["KnochenWächter"](image)
-                for pred in predictions:
-                    if pred['score'] >= conf_threshold:
-                        score_color = "#22c55e" if pred['score'] > 0.7 else "#eab308"
+            # Afficher uniquement si des fractures sont détectées
+            if fractures_found:
+                with col1 if idx % 2 == 0 else col2:
+                    result_image = image.copy()
+                    result_image = draw_boxes(result_image, predictions)
+                    st.image(result_image, caption=f"Bild {idx + 1}", use_column_width=True)
+                    
+                    # Analyse KnochenWächter et RöntgenMeister
+                    pred_wachter = models["KnochenWächter"](image)[0]
+                    pred_meister = models["RöntgenMeister"](image)[0]
+                    
+                    if pred_wachter['score'] > 0.6 or pred_meister['score'] > 0.6:
                         st.markdown(f"""
                             <div class='result-box'>
-                                <span style='color: {score_color}; font-weight: 500;'>
-                                    {pred['score']:.1%}
-                                </span> - {translate_label(pred['label'])}
+                                <span style='color: #2563eb'>KnochenWächter:</span> {pred_wachter['score']:.1%}<br>
+                                <span style='color: #2563eb'>RöntgenMeister:</span> {pred_meister['score']:.1%}
                             </div>
                         """, unsafe_allow_html=True)
-            
-            with col_right:
-                st.markdown("**🎓 Der RöntgenMeister**")
-                predictions = models["RöntgenMeister"](image)
-                for pred in predictions:
-                    if pred['score'] >= conf_threshold:
-                        score_color = "#22c55e" if pred['score'] > 0.7 else "#eab308"
-                        st.markdown(f"""
-                            <div class='result-box'>
-                                <span style='color: {score_color}; font-weight: 500;'>
-                                    {pred['score']:.1%}
-                                </span> - {translate_label(pred['label'])}
-                            </div>
-                        """, unsafe_allow_html=True)
-        else:
-            st.info("Bitte laden Sie ein Röntgenbild hoch (JPEG, PNG)")
-
-    # Script pour la synchronisation du thème
-    st.markdown("""
-        <script>
-            function updateTheme(isDark) {
-                document.documentElement.setAttribute('data-theme', isDark ? 'dark' : 'light');
-            }
-            
-            window.addEventListener('message', function(e) {
-                if (e.data.type === 'theme-change') {
-                    updateTheme(e.data.theme === 'dark');
-                }
-            });
-            
-            updateTheme(window.matchMedia('(prefers-color-scheme: dark)').matches);
-        </script>
-    """, unsafe_allow_html=True)
 
 if __name__ == "__main__":
     main()