Update app.py

app.py

@@ -1,111 +1,75 @@
 import streamlit as st
-from transformers import pipeline, AutoImageProcessor
+from transformers import pipeline
 from PIL import Image, ImageDraw
 import torch
 
+# Configuration de la page
 st.set_page_config(
-    page_title="Fraktur Detektion",
     layout="wide",
+    page_title="Fraktur Detektion",
     initial_sidebar_state="collapsed"
 )
 
-# Configuration du style personnalisé
+# Style personnalisé
 st.markdown("""
 <style>
+    /* Cacher les éléments Streamlit par défaut */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    
+    /* Style personnalisé pour la page */
     .stApp {
-        background-color: transparent !important;
+        margin: 0;
         padding: 0 !important;
+        max-width: 100%;
     }
     
-    [data-theme="light"] {
-        --background-color: #ffffff;
-        --text-color: #1f2937;
-        --border-color: #e5e7eb;
-        --button-color: #2563eb;
-        --button-hover: #1d4ed8;
-    }
-    
-    [data-theme="dark"] {
-        --background-color: #1f2937;
-        --text-color: #f3f4f6;
-        --border-color: #4b5563;
-        --button-color: #3b82f6;
-        --button-hover: #2563eb;
-    }
-    
+    /* Style pour les boutons */
     .stButton > button {
-        width: 100% !important;
-        background-color: var(--button-color) !important;
-        color: white !important;
-        border: none !important;
-        padding: 0.75rem 1.5rem !important;
-        border-radius: 0.375rem !important;
-        font-weight: 500 !important;
-        margin: 1rem 0 !important;
-        cursor: pointer !important;
+        width: 100%;
+        background-color: #3b82f6;
+        color: white;
+        padding: 0.5rem 1rem;
+        border-radius: 0.375rem;
+        border: none;
+        font-weight: 500;
     }
     
     .stButton > button:hover {
-        background-color: var(--button-hover) !important;
+        background-color: #2563eb;
     }
     
+    /* Container principal */
     .block-container {
-        padding: 0.5rem !important;
+        padding: 1rem !important;
         max-width: 100% !important;
     }
     
+    /* Style pour les images */
     .stImage > img {
-        max-height: 250px !important;
-        width: auto !important;
-        margin: 0 auto !important;
-    }
-    
-    .result-box {
-        padding: 0.375rem;
-        border-radius: 0.375rem;
-        margin: 0.25rem 0;
-        background: var(--background-color);
-        border: 1px solid var(--border-color);
-        color: var(--text-color);
-    }
-    
-    h2, h3, h4 {
-        margin: 0.5rem 0 !important;
-        color: var(--text-color) !important;
-        font-size: 1rem !important;
-    }
-    
-    #MainMenu, footer, header {
-        display: none !important;
+        max-height: 300px;
+        width: auto;
+        margin: 0 auto;
     }
     
-    .uploadedFile {
-        border: 1px dashed var(--border-color);
+    /* Style pour les messages d'erreur */
+    .stAlert {
+        padding: 1rem;
+        margin: 1rem 0;
         border-radius: 0.375rem;
-        padding: 0.25rem;
-    }
-    
-    div[data-testid="stFileUploader"] {
-        width: 100%;
-    }
-
-    /* Cache le message d'upload par défaut */
-    .uploadedFile small {
-        display: none !important;
     }
 </style>
 """, unsafe_allow_html=True)
 
+# Cache des modèles
 @st.cache_resource
 def load_models():
     return {
         "KnochenAuge": pipeline("object-detection", model="D3STRON/bone-fracture-detr"),
-        "KnochenWächter": pipeline("image-classification", 
-            model="Heem2/bone-fracture-detection-using-xray",
-            image_processor=AutoImageProcessor.from_pretrained("Heem2/bone-fracture-detection-using-xray")),
+        "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",
-            image_processor=AutoImageProcessor.from_pretrained("nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388"))
+            model="nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388")
     }
 
 def draw_boxes(image, predictions):
@@ -113,98 +77,73 @@ def draw_boxes(image, predictions):
     for pred in predictions:
         if pred['label'].lower() == 'fracture' and pred['score'] > 0.6:
             box = pred['box']
-            label = f"Fraktur ({pred['score']:.2%})"
-            color = "#2563eb"
+            label = f"Fraktur ({pred['score']:.1%})"
+            color = "#EF4444"  # Rouge
             
+            # Dessiner le rectangle
             draw.rectangle(
                 [(box['xmin'], box['ymin']), (box['xmax'], box['ymax'])],
                 outline=color,
                 width=2
             )
             
+            # Ajouter le label
             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():
-    # Initialisation de la session state si nécessaire
-    if 'models_loaded' not in st.session_state:
-        st.session_state.models_loaded = False
-        st.session_state.models = load_models()
-        st.session_state.models_loaded = True
-
-    # Créer deux colonnes pour l'upload et le bouton
-    col_upload, col_button = st.columns([3, 1])
-
-    with col_upload:
-        uploaded_files = st.file_uploader(
-            "Röntgenbilder hochladen",
-            type=['png', 'jpg', 'jpeg'],
-            accept_multiple_files=True,
-            label_visibility="collapsed"
-        )
-
-    # Bouton d'analyse dans la deuxième colonne
-    with col_button:
-        analyze_clicked = st.button("📋 Analysieren", key="analyze_btn", disabled=not uploaded_files)
-
-    # Analyse des images si le bouton est cliqué
-    if uploaded_files and analyze_clicked:
-        col1, col2 = st.columns([1, 1])
-        
-        for idx, uploaded_file in enumerate(uploaded_files):
-            image = Image.open(uploaded_file)
-            
-            # Analyse avec KnochenAuge (localisierung)
-            predictions = st.session_state.models["KnochenAuge"](image)
-            fractures_found = any(p['label'].lower() == 'fracture' and p['score'] > 0.6 for p in predictions)
-            
-            # 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 = st.session_state.models["KnochenWächter"](image)[0]
-                    pred_meister = st.session_state.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: #2563eb'>KnochenWächter:</span> {pred_wachter['score']:.1%}<br>
-                                <span style='color: #2563eb'>RöntgenMeister:</span> {pred_meister['score']:.1%}
-                            </div>
-                        """, unsafe_allow_html=True)
-
-# Script pour la synchronisation du thème
-    st.markdown("""
-        <script>
-            function updateTheme(isDark) {
-                document.documentElement.setAttribute('data-theme', isDark ? 'dark' : 'light');
-                const root = document.documentElement;
-                if (isDark) {
-                    root.style.setProperty('--background-color', '#1f2937');
-                    root.style.setProperty('--text-color', '#f3f4f6');
-                    root.style.setProperty('--border-color', '#4b5563');
-                } else {
-                    root.style.setProperty('--background-color', '#ffffff');
-                    root.style.setProperty('--text-color', '#1f2937');
-                    root.style.setProperty('--border-color', '#e5e7eb');
-                }
-            }
-            
-            window.addEventListener('message', function(e) {
-                if (e.data.type === 'theme-change') {
-                    updateTheme(e.data.theme === 'dark');
-                }
-            });
+    # Chargement des modèles
+    models = load_models()
+    
+    # Upload des images
+    uploaded_files = st.file_uploader(
+        "Röntgenbilder hochladen",
+        type=['png', 'jpg', 'jpeg'],
+        accept_multiple_files=True,
+        label_visibility="collapsed"
+    )
+    
+    if uploaded_files:
+        # Bouton d'analyse
+        if st.button("Bilder analysieren", key="analyze_button"):
+            # Création des colonnes pour l'affichage
+            cols = st.columns(2)
             
-            updateTheme(window.matchMedia('(prefers-color-scheme: dark)').matches);
-        </script>
-    """, unsafe_allow_html=True)
+            for idx, uploaded_file in enumerate(uploaded_files):
+                image = Image.open(uploaded_file)
+                
+                # Analyse avec KnochenAuge
+                predictions = models["KnochenAuge"](image)
+                fractures_found = any(p['label'].lower() == 'fracture' and p['score'] > 0.6 for p in predictions)
+                
+                if fractures_found:
+                    with cols[idx % 2]:
+                        # Créer une copie de l'image pour le dessin
+                        result_image = image.copy()
+                        result_image = draw_boxes(result_image, predictions)
+                        st.image(result_image, use_column_width=True)
+                        
+                        # Analyses supplémentaires
+                        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 style='background-color: #1F2937; color: white; padding: 1rem; border-radius: 0.375rem;'>
+                                    <div style='margin-bottom: 0.5rem;'>
+                                        <span style='color: #60A5FA;'>KnochenWächter:</span> {pred_wachter['score']:.1%}
+                                    </div>
+                                    <div>
+                                        <span style='color: #60A5FA;'>RöntgenMeister:</span> {pred_meister['score']:.1%}
+                                    </div>
+                                </div>
+                                """,
+                                unsafe_allow_html=True
+                            )
 
 if __name__ == "__main__":
     main()