Delete app.py

app.py

@@ -1,376 +0,0 @@
-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: #000000;
-        margin: 0;
-        padding: 0px;
-        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)