Update app.py

app.py

@@ -4,52 +4,12 @@ from PIL import Image, ImageDraw
 import numpy as np
 import colorsys
 
-# Configuration pour HuggingFace Spaces
 st.set_page_config(
     page_title="Fraktur Detektion",
     layout="wide",
     initial_sidebar_state="collapsed"
 )
 
-# Script pour gérer les WebSockets
-st.components.v1.html("""
-    <script>
-        // Fonction pour corriger la connexion WebSocket
-        function fixWebSocketConnection() {
-            const originalWebSocket = window.WebSocket;
-            window.WebSocket = function(url, protocols) {
-                if (url.includes('_stcore/stream')) {
-                    const newUrl = new URL(url);
-                    newUrl.pathname = '/_stcore/stream';
-                    url = newUrl.toString();
-                }
-                return new originalWebSocket(url, protocols);
-            };
-        }
-
-        // Configuration du WebSocket pour Edge et autres navigateurs
-        if (window.WebSocket) {
-            fixWebSocketConnection();
-            
-            // Gérer la reconnexion en cas d'erreur
-            window.addEventListener('load', function() {
-                const maxRetries = 5;
-                let retryCount = 0;
-                
-                function tryConnection() {
-                    if (retryCount < maxRetries) {
-                        fixWebSocketConnection();
-                        retryCount++;
-                        setTimeout(tryConnection, 2000);
-                    }
-                }
-                
-                tryConnection();
-            });
-        }
-    </script>
-""", height=0)
-
 st.markdown("""
 <style>
     .stApp {
@@ -136,25 +96,6 @@ st.markdown("""
     [data-testid="stExpander"], .element-container:has(>.stAlert) {
         display: none !important;
     }
-
-    /* Fix pour les iframes et WebSocket */
-    iframe {
-        display: block !important;
-        visibility: visible !important;
-        opacity: 1 !important;
-    }
-
-    .st-emotion-cache-1yiq2ps {
-        overflow: visible !important;
-    }
-
-    .st-emotion-cache-1dp5vir {
-        display: none !important;
-    }
-
-    .st-emotion-cache-r421ms {
-        z-index: 999999 !important;
-    }
 </style>
 """, unsafe_allow_html=True)
 
@@ -185,17 +126,21 @@ def create_heatmap_overlay(image, box, score):
     x1, y1 = box['xmin'], box['ymin']
     x2, y2 = box['xmax'], box['ymax']
     
+    # Couleur basée sur le score
     if score > 0.8:
-        fill_color = (255, 0, 0, 100)
+        fill_color = (255, 0, 0, 100)  # Rouge
         border_color = (255, 0, 0, 255)
     elif score > 0.6:
-        fill_color = (255, 165, 0, 100)
+        fill_color = (255, 165, 0, 100)  # Orange
         border_color = (255, 165, 0, 255)
     else:
-        fill_color = (255, 255, 0, 100)
+        fill_color = (255, 255, 0, 100)  # Jaune
         border_color = (255, 255, 0, 255)
     
+    # Rectangle semi-transparent
     draw.rectangle([x1, y1, x2, y2], fill=fill_color)
+    
+    # Bordure
     draw.rectangle([x1, y1, x2, y2], outline=border_color, width=2)
     
     return overlay
@@ -207,16 +152,20 @@ def draw_boxes(image, predictions):
         box = pred['box']
         score = pred['score']
         
+        # Création de l'overlay
         overlay = create_heatmap_overlay(image, box, score)
         result_image = Image.alpha_composite(result_image, overlay)
         
+        # Ajout du texte
         draw = ImageDraw.Draw(result_image)
         temp = 36.5 + (score * 2.5)
         label = f"{translate_label(pred['label'])} ({score:.1%} • {temp:.1f}°C)"
         
+        # Fond noir pour le texte
         text_bbox = draw.textbbox((box['xmin'], box['ymin']-20), label)
         draw.rectangle(text_bbox, fill=(0, 0, 0, 180))
         
+        # Texte en blanc
         draw.text(
             (box['xmin'], box['ymin']-20),
             label,
@@ -226,99 +175,100 @@ def draw_boxes(image, predictions):
     return result_image
 
 def main():
-    try:
-        models = load_models()
+    models = load_models()
+    
+    with st.container():
+        st.write("### 📤 Röntgenbild hochladen")
+        uploaded_file = st.file_uploader("Bild auswählen", type=['png', 'jpg', 'jpeg'], label_visibility="collapsed")
         
-        with st.container():
-            st.write("### 📤 Röntgenbild hochladen")
-            uploaded_file = st.file_uploader("Bild auswählen", type=['png', 'jpg', 'jpeg'], label_visibility="collapsed")
-            
-            col1, col2 = st.columns([2, 1])
-            with col1:
-                conf_threshold = st.slider(
-                    "Konfidenzschwelle",
-                    min_value=0.0, max_value=1.0,
-                    value=0.60, step=0.05,
-                    label_visibility="visible"
-                )
-            with col2:
-                analyze_button = st.button("Analysieren")
+        col1, col2 = st.columns([2, 1])
+        with col1:
+            conf_threshold = st.slider(
+                "Konfidenzschwelle",
+                min_value=0.0, max_value=1.0,
+                value=0.60, step=0.05,
+                label_visibility="visible"
+            )
+        with col2:
+            analyze_button = st.button("Analysieren")
 
-        if uploaded_file and analyze_button:
-            with st.spinner("Bild wird analysiert..."):
-                image = Image.open(uploaded_file)
-                results_container = st.container()
-                
-                predictions_watcher = models["KnochenWächter"](image)
-                predictions_master = models["RöntgenMeister"](image)
-                predictions_locator = models["KnochenAuge"](image)
-                
-                has_fracture = False
-                max_fracture_score = 0
-                filtered_locations = [p for p in predictions_locator 
-                                    if p['score'] >= conf_threshold]
-                
-                for pred in predictions_watcher:
-                    if pred['score'] >= conf_threshold and 'fracture' in pred['label'].lower():
-                        has_fracture = True
-                        max_fracture_score = max(max_fracture_score, pred['score'])
+    if uploaded_file and analyze_button:
+        with st.spinner("Bild wird analysiert..."):
+            image = Image.open(uploaded_file)
+            results_container = st.container()
+            
+            predictions_watcher = models["KnochenWächter"](image)
+            predictions_master = models["RöntgenMeister"](image)
+            predictions_locator = models["KnochenAuge"](image)
+            
+            has_fracture = False
+            max_fracture_score = 0
+            filtered_locations = [p for p in predictions_locator 
+                                if p['score'] >= conf_threshold]
+            
+            for pred in predictions_watcher:
+                if pred['score'] >= conf_threshold and 'fracture' in pred['label'].lower():
+                    has_fracture = True
+                    max_fracture_score = max(max_fracture_score, pred['score'])
+            
+            with results_container:
+                st.write("### 🔍 Analyse Ergebnisse")
+                col1, col2 = st.columns(2)
                 
-                with results_container:
-                    st.write("### 🔍 Analyse Ergebnisse")
-                    col1, col2 = st.columns(2)
+                with col1:
+                    st.write("#### 🤖 KI-Diagnose")
                     
-                    with col1:
-                        st.write("#### 🤖 KI-Diagnose")
-                        
-                        st.markdown("#### 🛡️ KnochenWächter")
-                        for pred in predictions_watcher:
-                            confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500'
-                            label_lower = pred['label'].lower()
-                            if pred['score'] >= conf_threshold and 'fracture' in label_lower:
-                                has_fracture = True
-                                max_fracture_score = max(max_fracture_score, pred['score'])
-                            st.markdown(f"""
-                                <div class="result-box" style="color: #1a1a1a;">
-                                    <span style="color: {confidence_color}; font-weight: 500;">
-                                        {pred['score']:.1%}
-                                    </span> - {translate_label(pred['label'])}
-                                </div>
-                            """, unsafe_allow_html=True)
-                        
-                        st.markdown("#### 🎓 RöntgenMeister")
-                        for pred in predictions_master:
-                            confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500'
-                            st.markdown(f"""
-                                <div class="result-box" style="color: #1a1a1a;">
-                                    <span style="color: {confidence_color}; font-weight: 500;">
-                                        {pred['score']:.1%}
-                                    </span> - {translate_label(pred['label'])}
-                                </div>
-                            """, unsafe_allow_html=True)
-                        
-                        if max_fracture_score > 0:
-                            st.write("#### 📊 Wahrscheinlichkeit")
-                            no_fracture_prob = 1 - max_fracture_score
-                            st.markdown(f"""
-                                <div class="result-box" style="color: #1a1a1a;">
-                                    Knochenbruch: <strong style="color: #0066cc">{max_fracture_score:.1%}</strong><br>
-                                    Kein Knochenbruch: <strong style="color: #ffa500">{no_fracture_prob:.1%}</strong>
-                                </div>
-                            """, unsafe_allow_html=True)
+                    st.markdown("#### 🛡️ KnochenWächter")
+                    # Afficher tous les résultats de KnochenWächter
+                    for pred in predictions_watcher:
+                        confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500'
+                        label_lower = pred['label'].lower()
+                        # Mettre à jour max_fracture_score seulement pour les fractures
+                        if pred['score'] >= conf_threshold and 'fracture' in label_lower:
+                            has_fracture = True
+                            max_fracture_score = max(max_fracture_score, pred['score'])
+                        # Afficher tous les résultats
+                        st.markdown(f"""
+                            <div class="result-box" style="color: #1a1a1a;">
+                                <span style="color: {confidence_color}; font-weight: 500;">
+                                    {pred['score']:.1%}
+                                </span> - {translate_label(pred['label'])}
+                            </div>
+                        """, unsafe_allow_html=True)
+                    
+                    st.markdown("#### 🎓 RöntgenMeister")
+                    # Afficher tous les résultats de RöntgenMeister
+                    for pred in predictions_master:
+                        confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500'
+                        st.markdown(f"""
+                            <div class="result-box" style="color: #1a1a1a;">
+                                <span style="color: {confidence_color}; font-weight: 500;">
+                                    {pred['score']:.1%}
+                                </span> - {translate_label(pred['label'])}
+                            </div>
+                        """, unsafe_allow_html=True)
+                    
+                    if max_fracture_score > 0:
+                        st.write("#### 📊 Wahrscheinlichkeit")
+                        no_fracture_prob = 1 - max_fracture_score
+                        st.markdown(f"""
+                            <div class="result-box" style="color: #1a1a1a;">
+                                Knochenbruch: <strong style="color: #0066cc">{max_fracture_score:.1%}</strong><br>
+                                Kein Knochenbruch: <strong style="color: #ffa500">{no_fracture_prob:.1%}</strong>
+                            </div>
+                        """, unsafe_allow_html=True)
+                
+                with col2:
+                    predictions = models["KnochenAuge"](image)
+                    filtered_preds = [p for p in predictions if p['score'] >= conf_threshold]
                     
-                    with col2:
-                        predictions = models["KnochenAuge"](image)
-                        filtered_preds = [p for p in predictions if p['score'] >= conf_threshold]
-                        
-                        if filtered_preds:
-                            st.write("#### 🎯 Fraktur Lokalisation")
-                            result_image = draw_boxes(image, filtered_preds)
-                            st.image(result_image, use_container_width=True)
-                        else:
-                            st.write("#### 🖼️ Röntgenbild")
-                            st.image(image, use_container_width=True)
-    except Exception as e:
-        st.error(f"Ein Fehler ist aufgetreten: {str(e)}")
+                    if filtered_preds:
+                        st.write("#### 🎯 Fraktur Lokalisation")
+                        result_image = draw_boxes(image, filtered_preds)
+                        st.image(result_image, use_container_width=True)
+                    else:
+                        st.write("#### 🖼️ Röntgenbild")
+                        st.image(image, use_container_width=True)
 
 if __name__ == "__main__":
     main()