app.py

import streamlit as st
from transformers import pipeline
from PIL import Image, ImageDraw
import torch

# Configuration page
st.set_page_config(
    page_title="Fraktur Detektion",
    layout="wide",
    initial_sidebar_state="collapsed"
)

# CSS optimisé
st.markdown("""
<style>
    /* Reset complet */
    .stApp {
        background-color: var(--background-color) !important;
        padding: 0 !important;
        max-height: 600px !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;
    }
    
    /* Conteneur principal */
    .block-container {
        padding: 0.5rem !important;
        max-width: 100% !important;
    }
    
    /* Upload et contrôles */
    .uploadedFile {
        border: 1px dashed var(--border-color);
        border-radius: 0.375rem;
        padding: 0.25rem;
        background: var(--secondary-bg);
    }
    
    /* Images plus petites */
    .stImage > img {
        max-width: 250px !important;
        max-height: 250px !important;
        margin: 0 auto !important;
    }
    
    /* Tabs compacts */
    .stTabs [data-baseweb="tab-list"] {
        gap: 0.25rem;
        background: transparent;
    }
    
    .stTabs [data-baseweb="tab"] {
        padding: 0.25rem 0.5rem;
        background: var(--secondary-bg);
        border-radius: 0.375rem;
    }
    
    /* Résultats */
    .result-box {
        padding: 0.375rem;
        border-radius: 0.375rem;
        margin: 0.25rem 0;
        background: var(--secondary-bg);
        border: 1px solid var(--border-color);
        color: var(--text-color);
    }
    
    /* Cacher éléments inutiles */
    #MainMenu, footer, header, .viewerBadge_container__1QSob, .stDeployButton {
        display: none !important;
    }
    
    div[data-testid="stVerticalBlock"] {
        gap: 0.5rem !important;
    }
    
    /* Ajustements espacement */
    .st-emotion-cache-1kyxreq {
        margin-top: -1rem !important;
    }
    
    .st-emotion-cache-1wmy9hl {
        padding: 0 !important;
    }
</style>
""", unsafe_allow_html=True)

@st.cache_resource
def load_models():
    return {
        "KnochenAuge": pipeline("object-detection", model="D3STRON/bone-fracture-detr"),  # L'œil des os
        "KnochenWächter": pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray"),  # Le gardien des os
        "RöntgenMeister": pipeline("image-classification",  # Le maître des rayons X
            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
        )
        
        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()
    
    # Contrôle de confiance compact
    conf_threshold = st.slider(
        "Konfidenzschwelle",
        min_value=0.0, max_value=1.0,
        value=0.60, step=0.05
    )
    
    # Upload plus propre
    uploaded_file = st.file_uploader("", type=['png', 'jpg', 'jpeg'])

    if uploaded_file:
        image = Image.open(uploaded_file)
        max_size = (250, 250)  # Taille réduite
        image.thumbnail(max_size, Image.Resampling.LANCZOS)

        tab1, tab2 = st.tabs(["📊 KI-Analyse", "🔍 Lokalisierung"])
        
        with tab1:
            # Afficher l'image originale seulement dans l'onglet Analyse
            st.image(image, use_container_width=False)
            
            model_names = {
                "KnochenWächter": "🛡️ Der KnochenWächter",
                "RöntgenMeister": "🎓 Der RöntgenMeister"
            }
            
            for model_key, display_name in model_names.items():
                st.markdown(f"<div style='font-weight:500; margin-top:0.5rem;'>{display_name}</div>", unsafe_allow_html=True)
                predictions = models[model_key](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)

        with tab2:
            # Dans l'onglet Lokalisierung, montrer directement l'image avec les boîtes
            with st.spinner("Analyse läuft..."):
                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.markdown("### 👁️ Das KnochenAuge")
                    st.image(result_image, use_container_width=False)
                else:
                    st.info("Keine Auffälligkeiten erkannt")

    else:
        st.info("Röntgenbild hochladen (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');
                }
            });
            
            // Thème initial
            updateTheme(window.matchMedia('(prefers-color-scheme: dark)').matches);
        </script>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()