app.py

import streamlit as st
from transformers import pipeline
from PIL import Image, ImageDraw
import numpy as np

# Configuration de la page
if 'page_config' not in st.session_state:
    st.set_page_config(
        page_title="Fraktur Detektion",
        layout="wide",
        initial_sidebar_state="collapsed",
        menu_items=None
    )
    st.session_state.page_config = True

@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"),
        "RöntgenMeister": pipeline("image-classification", 
            model="nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388")
    }

def translate_label(label):
    translations = {
        "fracture": "Knochenbruch",
        "no fracture": "Kein Knochenbruch",
        "normal": "Normal",
        "abnormal": "Auffällig",
        "F1": "Knochenbruch",
        "NF": "Kein Knochenbruch"
    }
    return translations.get(label.lower(), label)

def create_heatmap_overlay(image, box, score):
    overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
    draw = ImageDraw.Draw(overlay)
    
    x1, y1 = box['xmin'], box['ymin']
    x2, y2 = box['xmax'], box['ymax']
    
    if score > 0.8:
        fill_color = (255, 0, 0, 100)
        border_color = (255, 0, 0, 255)
    elif score > 0.6:
        fill_color = (255, 165, 0, 100)
        border_color = (255, 165, 0, 255)
    else:
        fill_color = (255, 255, 0, 100)
        border_color = (255, 255, 0, 255)
    
    draw.rectangle([x1, y1, x2, y2], fill=fill_color)
    draw.rectangle([x1, y1, x2, y2], outline=border_color, width=2)
    
    return overlay

def draw_boxes(image, predictions):
    result_image = image.copy().convert('RGBA')
    
    for pred in predictions:
        box = pred['box']
        score = pred['score']
        
        overlay = create_heatmap_overlay(image, box, score)
        result_image = Image.alpha_composite(result_image, overlay)
        
        draw = ImageDraw.Draw(result_image)
        temp = 36.5 + (score * 2.5)
        label = f"{translate_label(pred['label'])} ({score:.1%} • {temp:.1f}°C)"
        
        text_bbox = draw.textbbox((box['xmin'], box['ymin']-20), label)
        draw.rectangle(text_bbox, fill=(0, 0, 0, 180))
        
        draw.text(
            (box['xmin'], box['ymin']-20),
            label,
            fill=(255, 255, 255, 255)
        )
    
    return result_image

def main():
    st.markdown("""
        <style>
            .stApp {background: #f0f2f5}
            div[data-testid="stToolbar"] {display: none}
            #MainMenu {visibility: hidden}
            footer {visibility: hidden}
            header {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()
        
        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
            )
        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'])
                
                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">
                                    <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]
                        
                        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__":
    main()