Upload app.py

app.py

@@ -0,0 +1,403 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import HTMLResponse
+from transformers import pipeline
+from PIL import Image, ImageDraw
+import numpy as np
+import io
+import uvicorn
+import base64
+
+app = FastAPI()
+
+# Chargement des modèles
+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")
+    }
+
+models = load_models()
+
+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 image_to_base64(image):
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+    return f"data:image/png;base64,{img_str}"
+
+COMMON_STYLES = """
+    body {
+        font-family: system-ui, -apple-system, sans-serif;
+        background: #f0f2f5;
+        margin: 0;
+        padding: 20px;
+        color: #1a1a1a;
+    }
+    ::-webkit-scrollbar {
+        width: 8px;
+        height: 8px;
+    }
+    
+    ::-webkit-scrollbar-track {
+        background: transparent;
+    }
+    
+    ::-webkit-scrollbar-thumb {
+        background-color: rgba(156, 163, 175, 0.5);
+        border-radius: 4px;
+    }
+    
+    .container {
+        max-width: 1200px;
+        margin: 0 auto;
+        background: white;
+        padding: 20px;
+        border-radius: 10px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    .button {
+        background: #2d2d2d;
+        color: white;
+        border: none;
+        padding: 12px 30px;
+        border-radius: 8px;
+        cursor: pointer;
+        font-size: 1.1em;
+        transition: all 0.3s ease;
+        position: relative;
+    }
+    .button:hover {
+        background: #404040;
+    }
+    @keyframes progress {
+        0% { width: 0; }
+        100% { width: 100%; }
+    }
+    .button-progress {
+        position: absolute;
+        bottom: 0;
+        left: 0;
+        height: 4px;
+        background: rgba(255, 255, 255, 0.5);
+        width: 0;
+    }
+    .button:active .button-progress {
+        animation: progress 2s linear forwards;
+    }
+    img {
+        max-width: 100%;
+        height: auto;
+        border-radius: 8px;
+    }
+    @keyframes blink {
+        0% { opacity: 1; }
+        50% { opacity: 0; }
+        100% { opacity: 1; }
+    }
+    #loading {
+        display: none;
+        color: white;
+        margin-top: 10px;
+        animation: blink 1s infinite;
+        text-align: center;
+    }
+"""
+
+@app.get("/", response_class=HTMLResponse)
+async def main():
+    content = f"""
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>Fraktur Detektion</title>
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
+        <style>
+            {COMMON_STYLES}
+            
+            .upload-section {{
+                background: #2d2d2d;
+                padding: 40px;
+                border-radius: 12px;
+                margin: 20px 0;
+                text-align: center;
+                border: 2px dashed #404040;
+                transition: all 0.3s ease;
+                color: white;
+            }}
+            .upload-section:hover {{
+                border-color: #555;
+            }}
+            input[type="file"] {{
+                font-size: 1.1em;
+                margin: 20px 0;
+                color: white;
+            }}
+            input[type="file"]::file-selector-button {{
+                font-size: 1em;
+                padding: 10px 20px;
+                border-radius: 8px;
+                border: 1px solid #404040;
+                background: #2d2d2d;
+                color: white;
+                transition: all 0.3s ease;
+                cursor: pointer;
+            }}
+            input[type="file"]::file-selector-button:hover {{
+                background: #404040;
+            }}
+            .confidence-slider {{
+                width: 100%;
+                max-width: 300px;
+                margin: 20px auto;
+            }}
+            input[type="range"] {{
+                width: 100%;
+                height: 8px;
+                border-radius: 4px;
+                background: #404040;
+                outline: none;
+                transition: all 0.3s ease;
+                -webkit-appearance: none;
+            }}
+            input[type="range"]::-webkit-slider-thumb {{
+                -webkit-appearance: none;
+                width: 20px;
+                height: 20px;
+                border-radius: 50%;
+                background: white;
+                cursor: pointer;
+                border: none;
+            }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <div class="upload-section">
+                <form action="/analyze" method="post" enctype="multipart/form-data" onsubmit="document.getElementById('loading').style.display = 'block';">
+                    <div>
+                        <input type="file" name="file" accept="image/*" required>
+                    </div>
+                    <div class="confidence-slider">
+                        <label for="threshold">Konfidenzschwelle: <span id="thresholdValue">0.60</span></label>
+                        <input type="range" id="threshold" name="threshold" 
+                               min="0" max="1" step="0.05" value="0.60" 
+                               oninput="document.getElementById('thresholdValue').textContent = parseFloat(this.value).toFixed(2)">
+                    </div>
+                    <button type="submit" class="button">
+                        Analysieren
+                        <div class="button-progress"></div>
+                    </button>
+                    <div id="loading">Loading...</div>
+                </form>
+            </div>
+        </div>
+    </body>
+    </html>
+    """
+    return content
+
+@app.post("/analyze", response_class=HTMLResponse)
+async def analyze_file(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
+        
+        predictions_watcher = models["KnochenWächter"](image)
+        predictions_master = models["RöntgenMeister"](image)
+        predictions_locator = models["KnochenAuge"](image)
+        
+        filtered_preds = [p for p in predictions_locator if p['score'] >= 0.6]
+        if filtered_preds:
+            result_image = draw_boxes(image, filtered_preds)
+        else:
+            result_image = image
+
+        result_image_b64 = image_to_base64(result_image)
+        
+        results_html = f"""
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <title>Ergebnisse</title>
+            <meta name="viewport" content="width=device-width, initial-scale=1.0">
+            <style>
+                {COMMON_STYLES}
+                
+                .results-grid {{
+                    display: grid;
+                    grid-template-columns: 1fr 1fr;
+                    gap: 20px;
+                    margin-top: 20px;
+                }}
+                .result-box {{
+                    background: white;
+                    padding: 20px;
+                    border-radius: 12px;
+                    margin: 10px 0;
+                    border: 1px solid #e9ecef;
+                }}
+                .score-high {{ 
+                    color: #0066cc; 
+                    font-weight: bold;
+                }}
+                
+                .score-medium {{ 
+                    color: #ffa500; 
+                    font-weight: bold;
+                }}
+                .back-button {{
+                    display: inline-block;
+                    text-decoration: none;
+                    margin-top: 20px;
+                }}
+                h3 {{
+                    color: #0066cc;
+                    margin-top: 0;
+                }}
+                @media (max-width: 768px) {{
+                    .results-grid {{
+                        grid-template-columns: 1fr;
+                    }}
+                }}
+            </style>
+        </head>
+        <body>
+            <div class="container">
+                <div class="results-grid">
+                    <div>
+                        <div class="result-box"><h3>KnochenWächter</h3>
+        """
+        
+        for pred in predictions_watcher:
+            confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium"
+            results_html += f"""
+                <div>
+                    <span class="{confidence_class}">{pred['score']:.1%}</span> - 
+                    {translate_label(pred['label'])}
+                </div>
+            """
+        results_html += "</div>"
+        
+        results_html += "<div class='result-box'><h3>RöntgenMeister</h3>"
+        for pred in predictions_master:
+            confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium"
+            results_html += f"""
+                <div>
+                    <span class="{confidence_class}">{pred['score']:.1%}</span> - 
+                    {translate_label(pred['label'])}
+                </div>
+            """
+        results_html += "</div></div>"
+        
+        results_html += f"""
+                    <div class='result-box'>
+                        <h3>Fraktur Lokalisation</h3>
+                        <img src="{result_image_b64}" alt="Analyzed image">
+                    </div>
+                </div>
+                
+                <a href="/" class="button back-button">
+                    ← Zurück
+                    <div class="button-progress"></div>
+                </a>
+            </div>
+        </body>
+        </html>
+        """
+        
+        return results_html
+        
+    except Exception as e:
+        return f"""
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <title>Fehler</title>
+            <meta name="viewport" content="width=device-width, initial-scale=1.0">
+            <style>
+                {COMMON_STYLES}
+                .error-box {{
+                    background: #fee2e2;
+                    border: 1px solid #ef4444;
+                    padding: 20px;
+                    border-radius: 8px;
+                    margin: 20px 0;
+                }}
+            </style>
+        </head>
+        <body>
+            <div class="container">
+                <div class="error-box">
+                    <h3>Fehler</h3>
+                    <p>{str(e)}</p>
+                </div>
+                <a href="/" class="button back-button">
+                    ← Zurück
+                    <div class="button-progress"></div>
+                </a>
+            </div>
+        </body>
+        </html>
+        """
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)