Create app.py

app.py

@@ -0,0 +1,378 @@
+ from fastapi import FastAPI, File, UploadFile, Form
+ from fastapi.responses import HTMLResponse, Response
+ from transformers import pipeline
+ from PIL import Image, ImageDraw
+ import numpy as np
+ import io
+ import uvicorn
+ 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, black
+ 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: #404040; /* Changed button background color */
+         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: #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__":
+     uvicorn.run(app, host="0.0.0.0", port=7860)
+ 
+