Create app.py

app.py

@@ -0,0 +1,376 @@
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import HTMLResponse, Response
+from transformers import pipeline
+from PIL import Image, ImageDraw
+import io
+import base64
+from reportlab.lib.pagesizes import letter
+from reportlab.platypus import SimpleDocTemplate, Image as ReportLabImage, Paragraph, Spacer
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.colors import red, blue
+from reportlab.lib.units import inch
+
+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}"
+
+def generate_report(patient_name, analyzed_image_bytes, prediction, confidence):
+    buffer = io.BytesIO()
+    doc = SimpleDocTemplate(buffer, pagesize=letter)
+    styles = getSampleStyleSheet()
+    title_style = ParagraphStyle(
+        name='TitleStyle',
+        parent=styles['Normal'],
+        fontSize=16,
+        textColor=blue,
+        alignment=1  # Center alignment
+    )
+    heading_style = ParagraphStyle(
+        name='HeadingStyle',
+        parent=styles['Normal'],
+        fontSize=12,
+        textColor=red
+    )
+    prediction_style = ParagraphStyle(
+        name='PredictionStyle',
+        parent=styles['Normal'],
+        fontSize=14,
+        alignment=1
+    )
+
+    story = []
+
+    # Hospital Name
+    hospital_name = Paragraph("youesh hospital , mumbai ( west )", title_style)
+    story.append(hospital_name)
+    story.append(Spacer(1, 0.2*inch))
+
+    # Patient Greeting
+    greeting = Paragraph(f"hello , {patient_name} thank you for using our services this is your radiology report", heading_style)
+    story.append(greeting)
+    story.append(Spacer(1, 0.2*inch))
+
+    # Horizontal Line
+    story.append(Paragraph("<hr/>", styles['Normal']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Analyzed Image
+    img = ReportLabImage(io.BytesIO(analyzed_image_bytes), width=400, height=400, kind='direct')
+    story.append(img)
+    story.append(Spacer(1, 0.2*inch))
+
+    # Prediction
+    prediction_text = f"<b>Prediction:</b> {prediction.capitalize()}"
+    confidence_text = f"<b>Confidence:</b> {'Yes' if confidence > 0.6 else 'No'}"
+    story.append(Paragraph(prediction_text, prediction_style))
+    story.append(Paragraph(confidence_text, prediction_style))
+
+    doc.build(story)
+    buffer.seek(0)
+    return buffer.getvalue()
+
+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-image: url('https://i.imgur.com/your-image.png');
+        background-repeat: repeat;
+        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-color: #555;
+    }
+    @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}
+
+            .input-group {{
+                margin-bottom: 20px;
+            }}
+            .input-group label {{
+                display: block;
+                margin-bottom: 5px;
+                color: #404040;
+                font-weight: bold;
+            }}
+            .input-group input[type="text"] {{
+                width: calc(100% - 22px);
+                padding: 10px;
+                border: 1px solid #ccc;
+                border-radius: 4px;
+                font-size: 1em;
+            }}
+
+            .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;
+            }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <form action="/analyze" method="post" enctype="multipart/form-data" onsubmit="document.getElementById('loading').style.display = 'block';">
+                <div class="input-group">
+                    <label for="name">Name:</label>
+                    <input type="text" id="name" name="name" required>
+                </div>
+                <div class="upload-section">
+                    <div>
+                        <input type="file" name="file" accept="image/*" required>
+                    </div>
+                    <button type="submit" class="button">
+                        Generate Report
+                        <div class="button-progress"></div>
+                    </button>
+                    <div id="loading">Loading...</div>
+                </div>
+            </form>
+        </div>
+    </body>
+    </html>
+    """
+    return content
+
+@app.post("/analyze", response_class=Response)
+async def analyze_file(name: str = Form(...), file: UploadFile = File(...), threshold: float = Form(0.6)):
+    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'] >= threshold]
+        analyzed_image = image
+        overall_prediction = "No Fracture"
+        max_confidence = 0.0
+
+        if filtered_preds:
+            analyzed_image = draw_boxes(image, filtered_preds)
+            overall_prediction = "Fracture Detected"
+            max_confidence = max([p['score'] for p in filtered_preds])
+
+        image_stream = io.BytesIO()
+        analyzed_image.save(image_stream, format="PNG")
+        image_bytes = image_stream.getvalue()
+
+        pdf_report = generate_report(name, image_bytes, overall_prediction, max_confidence)
+
+        headers = {
+            'Content-Disposition': 'attachment; filename="report.pdf"'
+        }
+        return Response(content=pdf_report, headers=headers, media_type="application/pdf")
+
+    except Exception as e:
+        error_html = 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>
+        """
+        return HTMLResponse(content=error_html)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)