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ftx7go commited on Mar 9

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-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(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)