Update app.py

app.py

@@ -4,120 +4,27 @@ from PIL import Image, ImageDraw
 import numpy as np
 import os
 
-# Configuration basique de Streamlit pour HF Spaces
+# Configuration de base pour HF Spaces
+os.environ["STREAMLIT_SERVER_PORT"] = "7860"
+os.environ["STREAMLIT_SERVER_ADDRESS"] = "0.0.0.0"
+os.environ["STREAMLIT_SERVER_HEADLESS"] = "true"
+os.environ["STREAMLIT_SERVER_ENABLE_CORS"] = "true"
+
+# Configuration de la page
 st.set_page_config(
     page_title="Fraktur Detektion",
     layout="wide",
-    initial_sidebar_state="collapsed",
-    menu_items=None
+    initial_sidebar_state="collapsed"
 )
 
-def load_css():
-    st.markdown("""
-        <style>
-            .stApp {
-                background: #f0f2f5 !important;
-            }
-            
-            .block-container {
-                padding-top: 0 !important;
-                padding-bottom: 0 !important;
-                max-width: 1400px !important;
-            }
-            
-            .upload-container {
-                background: white;
-                padding: 1.5rem;
-                border-radius: 10px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-                margin-bottom: 1rem;
-                text-align: center;
-            }
-            
-            .results-container {
-                background: white;
-                padding: 1.5rem;
-                border-radius: 10px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-            }
-            
-            .result-box {
-                background: #f8f9fa;
-                padding: 0.75rem;
-                border-radius: 8px;
-                margin: 0.5rem 0;
-                border: 1px solid #e9ecef;
-            }
-            
-            h1, h2, h3, h4, p {
-                color: #1a1a1a !important;
-                margin: 0.5rem 0 !important;
-            }
-            
-            .stImage {
-                background: white;
-                padding: 0.5rem;
-                border-radius: 8px;
-                box-shadow: 0 1px 3px rgba(0,0,0,0.1);
-            }
-            
-            .stImage > img {
-                max-height: 300px !important;
-                width: auto !important;
-                margin: 0 auto !important;
-                display: block !important;
-            }
-            
-            [data-testid="stFileUploader"] {
-                width: 100% !important;
-            }
-            
-            .stFileUploaderFileName {
-                color: #1a1a1a !important;
-            }
-            
-            .stButton > button {
-                width: 200px;
-                background-color: #f8f9fa !important;
-                color: #1a1a1a !important;
-                border: 1px solid #e9ecef !important;
-                padding: 0.5rem 1rem !important;
-                border-radius: 5px !important;
-                transition: all 0.3s ease !important;
-            }
-            
-            .stButton > button:hover {
-                background-color: #e9ecef !important;
-                transform: translateY(-1px);
-            }
-            
-            #MainMenu, footer, header {
-                display: none !important;
-            }
-            
-            section[data-testid="stSidebar"] {
-                display: none !important;
-            }
-            
-            /* Hide deprecation warning */
-            [data-testid="stExpander"], .element-container:has(>.stAlert) {
-                display: none !important;
-            }
-        </style>
-    """, unsafe_allow_html=True)
-
-@st.cache_resource(show_spinner=True)
+@st.cache_resource
 def load_models():
-    try:
-        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")
-        }
-    except Exception as e:
-        st.error(f"Fehler beim Laden der Modelle: {str(e)}")
-        return None
+    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")
+    }
 
 def translate_label(label):
     translations = {
@@ -178,117 +85,114 @@ def draw_boxes(image, predictions):
     return result_image
 
 def main():
-    load_css()
+    st.markdown("""
+        <style>
+            .stApp {background: #f0f2f5}
+            .block-container {max-width: 1400px; padding-top: 1rem; padding-bottom: 1rem}
+            div[data-testid="stToolbar"] {display: none}
+            #MainMenu {visibility: hidden}
+            footer {visibility: hidden}
+            .result-box {
+                background: #f8f9fa;
+                padding: 0.75rem;
+                border-radius: 8px;
+                margin: 0.5rem 0;
+                border: 1px solid #e9ecef;
+            }
+        </style>
+    """, unsafe_allow_html=True)
     
     try:
         models = load_models()
-        if not models:
-            st.error("Die Anwendung konnte nicht korrekt initialisiert werden.")
-            return
-
-        col_main = st.container()
-        with col_main:
-            st.write("### 📤 Röntgenbild hochladen")
-            uploaded_file = st.file_uploader(
-                "Bild auswählen",
-                type=['png', 'jpg', 'jpeg'],
-                label_visibility="collapsed"
+        
+        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
             )
-            
-            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", use_container_width=True)
+        with col2:
+            analyze_button = st.button("Analysieren")
 
-            if uploaded_file and analyze_button:
-                with st.spinner("Bild wird analysiert..."):
-                    try:
-                        image = Image.open(uploaded_file)
-                        
-                        results_container = st.container()
-                        
-                        with st.spinner("Analyse läuft..."):
-                            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]
+        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 col1:
+                        st.write("#### 🤖 KI-Diagnose")
                         
+                        st.markdown("#### 🛡️ KnochenWächter")
                         for pred in predictions_watcher:
-                            if pred['score'] >= conf_threshold and 'fracture' in pred['label'].lower():
+                            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">
+                                    <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">
+                                    <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">
+                                    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 results_container:
-                            st.write("### 🔍 Analyse Ergebnisse")
-                            col1, col2 = st.columns(2)
-                            
-                            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)
-                            
-                            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"Fehler bei der Bildanalyse: {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)
     except Exception as e:
         st.error(f"Ein Fehler ist aufgetreten: {str(e)}")
 
 if __name__ == "__main__":
-    st.set_page_config(page_title="Fraktur Detektion", layout="wide", initial_sidebar_state="collapsed")
     main()