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""" Fraktur Detektion
Loading...
""" 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""" Ergebnisse

KnochenWächter

""" for pred in predictions_watcher: confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium" results_html += f"""
{pred['score']:.1%} - {translate_label(pred['label'])}
""" results_html += "
" results_html += "

RöntgenMeister

" for pred in predictions_master: confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium" results_html += f"""
{pred['score']:.1%} - {translate_label(pred['label'])}
""" results_html += "
" results_html += f"""

Fraktur Lokalisation

Analyzed image
← Zurück
""" return results_html except Exception as e: return f""" Fehler

Fehler

{str(e)}

← Zurück
""" if __name__ == "__main__": uvicorn.run(aimport gradio as gr import tensorflow as tf import numpy as np from tensorflow.keras.preprocessing import image from PIL import Image from reportlab.lib.pagesizes import letter from reportlab.pdfgen import canvas import os # Load the trained model once model = tf.keras.models.load_model("my_keras_model.h5") image_size = (224, 224) # Ensure consistent image size # Function to analyze injury severity def analyze_injury(prediction): if prediction < 0.3: return "Mild", "Rest and pain relief.", "₹2,000 - ₹5,000", "₹10,000 - ₹20,000" elif 0.3 <= prediction < 0.7: return "Moderate", "Plaster cast or minor surgery.", "₹8,000 - ₹15,000", "₹30,000 - ₹60,000" else: return "Severe", "Major surgery with metal implants.", "₹20,000 - ₹50,000", "₹1,00,000+" # Function to generate report def generate_report(patient_name, age, gender, xray1_path, xray2_path): if not os.path.exists(xray1_path) or not os.path.exists(xray2_path): return "Error: One or both X-ray images are missing!" try: # Process X-ray 1 img1 = Image.open(xray1_path).resize(image_size).convert("RGB") img_array1 = image.img_to_array(img1) img_array1 = np.expand_dims(img_array1, axis=0) / 255.0 prediction1 = model.predict(img_array1)[0][0] # Process X-ray 2 img2 = Image.open(xray2_path).resize(image_size).convert("RGB") img_array2 = image.img_to_array(img2) img_array2 = np.expand_dims(img_array2, axis=0) / 255.0 prediction2 = model.predict(img_array2)[0][0] # Get final analysis avg_prediction = (prediction1 + prediction2) / 2 predicted_class = "Fractured" if avg_prediction > 0.5 else "Normal" severity, treatment, gov_cost, private_cost = analyze_injury(avg_prediction) # Generate PDF Report report_path = f"{patient_name}_fracture_report.pdf" c = canvas.Canvas(report_path, pagesize=letter) c.setFont("Helvetica", 12) c.drawString(100, 750, f"Patient Name: {patient_name}") c.drawString(100, 730, f"Age: {age}") c.drawString(100, 710, f"Gender: {gender}") c.drawString(100, 690, f"Diagnosis: {predicted_class}") c.drawString(100, 670, f"Injury Severity: {severity}") c.drawString(100, 650, f"Recommended Treatment: {treatment}") c.drawString(100, 630, f"Estimated Cost (Govt Hospital): {gov_cost}") c.drawString(100, 610, f"Estimated Cost (Private Hospital): {private_cost}") c.save() if os.path.exists(report_path): return report_path else: return "Error: Report generation failed!" except Exception as e: return f"Error generating report: {str(e)}" # Define Gradio Interface interface = gr.Interface( fn=generate_report, inputs=[ gr.Textbox(label="Patient Name"), gr.Number(label="Age"), gr.Radio(["Male", "Female", "Other"], label="Gender"), gr.Image(type="filepath", label="Upload X-ray Image 1"), gr.Image(type="filepath", label="Upload X-ray Image 2"), ], outputs=gr.File(label="Download Report"), title="Bone Fracture Detection & Medical Report", description="Enter patient details, upload two X-ray images, and generate a detailed medical report with treatment suggestions and cost estimates." ) if __name__ == "__main__": interfacepp, host="0.0.0.0", port=7860)