Update app.py

app.py

@@ -120,7 +120,6 @@ def create_heatmap_overlay(image, box, score):
     draw = ImageDraw.Draw(overlay)
     
     def get_temp_color(value):
-        # Rouge pour valeurs élevées, bleu pour faibles
         if value > 0.8:
             return (255, 0, 0)  # Rouge vif
         elif value > 0.6:
@@ -135,14 +134,12 @@ def create_heatmap_overlay(image, box, score):
     width = x2 - x1
     height = y2 - y1
     
-    # Créer un effet de gradient radial
     steps = 30
     for i in range(steps):
         alpha = int(255 * (1 - (i / steps)) * 0.7)
         base_color = get_temp_color(score)
         color = base_color + (alpha,)
         
-        # Effet radial
         shrink_x = (i * width) / (steps * 2)
         shrink_y = (i * height) / (steps * 2)
         
@@ -152,7 +149,6 @@ def create_heatmap_overlay(image, box, score):
             outline=None
         )
     
-    # Ajouter une bordure plus visible
     border_color = get_temp_color(score) + (200,)
     draw.rectangle([x1, y1, x2, y2], outline=border_color, width=2)
     
@@ -161,23 +157,19 @@ def create_heatmap_overlay(image, box, score):
 def draw_boxes(image, predictions):
     result_image = image.copy().convert('RGBA')
     
-    # Trier les prédictions par score pour afficher les plus fortes en dernier
     sorted_predictions = sorted(predictions, key=lambda x: x['score'])
     
     for pred in sorted_predictions:
         box = pred['box']
         score = pred['score']
         
-        # Créer et appliquer la carte thermique
         heatmap = create_heatmap_overlay(image, box, score)
         result_image = Image.alpha_composite(result_image, heatmap)
         
-        # Ajouter le label avec la température
         draw = ImageDraw.Draw(result_image)
-        temp = 36.5 + (score * 2.5)  # Simulation de température: 36.5°C - 39°C
+        temp = 36.5 + (score * 2.5)
         label = f"{translate_label(pred['label'])} ({score:.1%}) • {temp:.1f}°C"
         
-        # Background du texte avec dégradé
         text_bbox = draw.textbbox((box['xmin'], box['ymin']-25), label)
         padding = 3
         text_bbox = (
@@ -186,7 +178,6 @@ def draw_boxes(image, predictions):
         )
         draw.rectangle(text_bbox, fill="#000000CC")
         
-        # Texte
         draw.text(
             (box['xmin'], box['ymin']-25),
             label,
@@ -216,90 +207,82 @@ def main():
             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()
-                
-                # Récupération de toutes les prédictions d'abord
-                predictions_watcher = models["KnochenWächter"](image)
-                predictions_master = models["RöntgenMeister"](image)
-                predictions_locator = models["KnochenAuge"](image)
-                
-                # Filtrage et traitement des résultats
-                has_fracture = False
-                max_fracture_score = 0
-                filtered_locations = [p for p in predictions_locator 
-                                    if p['score'] >= conf_threshold 
-                                    and 'fracture' in p['label'].lower()]
-                
-                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 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 
+                                and 'fracture' in p['label'].lower()]
+            
+            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)
                 
-                # Affichage des résultats
-                with results_container:
-                    st.write("### 🔍 Analyse Ergebnisse")
-                    col1, col2 = st.columns(2)
+                with col1:
+                    st.write("#### 🤖 KI-Diagnose")
                     
-                    with col1:
-                        st.write("#### 🤖 KI-Diagnose")
-                        
-                        # KnochenWächter
-                        st.write("##### 🛡️ KnochenWächter")
-                for pred in predictions_watcher:
-                    if pred['score'] >= conf_threshold:
-                        confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500'
-                        label_lower = pred['label'].lower()
-                        if '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)
-                
-                # RöntgenMeister
-                st.write("#### 🎓 RöntgenMeister")
-                predictions_master = models["RöntgenMeister"](image)
-                for pred in predictions_master:
-                    if pred['score'] >= conf_threshold:
-                        confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500'
+                    st.write("##### 🛡️ KnochenWächter")
+                    for pred in predictions_watcher:
+                        if pred['score'] >= conf_threshold:
+                            confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500'
+                            label_lower = pred['label'].lower()
+                            if '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.write("#### 🎓 RöntgenMeister")
+                    for pred in predictions_master:
+                        if pred['score'] >= conf_threshold:
+                            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;">
-                                <span style="color: {confidence_color}; font-weight: 500;">
-                                    {pred['score']:.1%}
-                                </span> - {translate_label(pred['label'])}
+                                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)
                 
-                # Probabilités actualisées
-                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:
-                # Vérification et localisation des fractures
-                predictions = models["KnochenAuge"](image)
-                filtered_preds = [p for p in predictions if p['score'] >= conf_threshold 
-                                and 'fracture' in p['label'].lower()]
-                
-                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)
+                with col2:
+                    predictions = models["KnochenAuge"](image)
+                    filtered_preds = [p for p in predictions if p['score'] >= conf_threshold 
+                                    and 'fracture' in p['label'].lower()]
+                    
+                    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() 
+    main()