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Update app.py
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app.py
CHANGED
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@@ -2,9 +2,10 @@ from fastapi import FastAPI, File, UploadFile
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from fastapi.responses import HTMLResponse
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from transformers import pipeline
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from PIL import Image, ImageDraw
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import io
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import base64
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import uvicorn
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app = FastAPI()
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@@ -19,7 +20,6 @@ def load_models():
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models = load_models()
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# Fonctions d'analyse existantes restent identiques
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def translate_label(label):
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translations = {
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"fracture": "Knochenbruch",
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@@ -31,20 +31,59 @@ def translate_label(label):
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}
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return translations.get(label.lower(), label)
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# Autres fonctions helper restent identiques
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def create_heatmap_overlay(image, box, score):
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def draw_boxes(image, predictions):
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def image_to_base64(image):
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# Page d'accueil simplifiée
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@app.get("/", response_class=HTMLResponse)
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async def main():
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content = """
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@@ -53,37 +92,47 @@ async def main():
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<head>
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<title>Fraktur Detektion</title>
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<style>
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:root { color-scheme: light dark; }
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body {
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font-family: system-ui, -apple-system, sans-serif;
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margin: 0;
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padding:
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overflow-x: hidden;
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}
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}
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.container {
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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}
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.upload-section {
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background: #
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padding:
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border-radius:
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margin:
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text-align: center;
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}
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background: #0066cc;
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color: white;
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border: none;
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padding:
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border-radius:
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cursor: pointer;
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.results-container {
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display: grid;
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gap: 1rem;
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margin-top: 1rem;
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}
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img {
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max-width: 100%;
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height: auto;
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border-radius: 0.5rem;
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}
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::-webkit-scrollbar {
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width: 8px;
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height: 8px;
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}
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background:
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}
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}
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background-color: rgba(255, 255, 255, 0.2);
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}
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}
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</style>
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</head>
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<body>
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<div class="container">
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<h2>Fraktur Detektion</h2>
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<div class="upload-section">
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<form action="/analyze" method="post" enctype="multipart/form-data">
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<
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<button type="submit" class="button">Analysieren</button>
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</form>
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</div>
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"""
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return content
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# Page de résultats simplifiée
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@app.post("/analyze", response_class=HTMLResponse)
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async def analyze_file(file: UploadFile = File(...)):
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try:
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# Votre logique d'analyse existante
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contents = await file.read()
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image = Image.open(io.BytesIO(contents))
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predictions_locator = models["KnochenAuge"](image)
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filtered_preds = [p for p in predictions_locator if p['score'] >= 0.6]
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result_image_b64 = image_to_base64(result_image)
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results_html = """
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<head>
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<title>Ergebnisse</title>
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<style>
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:root { color-scheme: light dark; }
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body {
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font-family: system-ui, -apple-system, sans-serif;
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margin: 0;
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padding:
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}
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}
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.container {
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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}
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.results-grid {
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display: grid;
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}
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.result-box {
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background: #
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padding:
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border-radius:
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}
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.back-button {
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display: inline-block;
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background: #0066cc;
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color: white;
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padding:
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border-radius:
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text-decoration: none;
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margin-top:
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}
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img {
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max-width: 100%;
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height:
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border-radius:
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::-webkit-scrollbar {
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width: 8px;
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height: 8px;
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}
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::-webkit-scrollbar-track {
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background: transparent;
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}
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::-webkit-scrollbar-thumb {
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background-color: rgba(0, 0, 0, 0.2);
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border-radius: 4px;
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}
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@media (prefers-color-scheme: dark) {
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::-webkit-scrollbar-thumb {
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background-color: rgba(255, 255, 255, 0.2);
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}
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}
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</style>
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</head>
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<body>
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<div class="container">
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<h2>Analyse Ergebnisse</h2>
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<div class="results-grid">
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"""
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# KnochenWächter results
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results_html += "<
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for pred in predictions_watcher:
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# RöntgenMeister results
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results_html += "<
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for pred in predictions_master:
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# Image result
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results_html += f"""
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<h3>Fraktur Lokalisation</h3>
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<img src="{result_image_b64}" alt="Analyzed image">
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</div>
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<head>
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<title>Fehler</title>
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<style>
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body {{
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font-family: system-ui, -apple-system, sans-serif;
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margin:
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}}
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</style>
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</head>
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<body>
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<
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<p>{str(e)}</p>
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<a href="/" class="back-button">← Zurück</a>
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</body>
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from fastapi.responses import HTMLResponse
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from transformers import pipeline
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from PIL import Image, ImageDraw
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import numpy as np
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import io
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import uvicorn
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import base64
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app = FastAPI()
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models = load_models()
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def translate_label(label):
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translations = {
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"fracture": "Knochenbruch",
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}
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return translations.get(label.lower(), label)
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def create_heatmap_overlay(image, box, score):
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overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
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draw = ImageDraw.Draw(overlay)
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x1, y1 = box['xmin'], box['ymin']
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x2, y2 = box['xmax'], box['ymax']
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if score > 0.8:
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fill_color = (255, 0, 0, 100)
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border_color = (255, 0, 0, 255)
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elif score > 0.6:
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fill_color = (255, 165, 0, 100)
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border_color = (255, 165, 0, 255)
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else:
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fill_color = (255, 255, 0, 100)
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border_color = (255, 255, 0, 255)
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draw.rectangle([x1, y1, x2, y2], fill=fill_color)
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draw.rectangle([x1, y1, x2, y2], outline=border_color, width=2)
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return overlay
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def draw_boxes(image, predictions):
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result_image = image.copy().convert('RGBA')
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for pred in predictions:
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box = pred['box']
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score = pred['score']
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overlay = create_heatmap_overlay(image, box, score)
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result_image = Image.alpha_composite(result_image, overlay)
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draw = ImageDraw.Draw(result_image)
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temp = 36.5 + (score * 2.5)
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label = f"{translate_label(pred['label'])} ({score:.1%} • {temp:.1f}°C)"
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text_bbox = draw.textbbox((box['xmin'], box['ymin']-20), label)
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draw.rectangle(text_bbox, fill=(0, 0, 0, 180))
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draw.text(
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(box['xmin'], box['ymin']-20),
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label,
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fill=(255, 255, 255, 255)
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)
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return result_image
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def image_to_base64(image):
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buffered = io.BytesIO()
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image.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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return f"data:image/png;base64,{img_str}"
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@app.get("/", response_class=HTMLResponse)
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async def main():
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content = """
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<head>
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<title>Fraktur Detektion</title>
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<style>
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body {
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font-family: system-ui, -apple-system, sans-serif;
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background: #f0f2f5;
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margin: 0;
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padding: 20px;
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color: #1a1a1a;
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}
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/* Scrollbar personnalisée */
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::-webkit-scrollbar {
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width: 8px;
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height: 8px;
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}
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::-webkit-scrollbar-track {
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background: transparent;
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}
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::-webkit-scrollbar-thumb {
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background-color: rgba(156, 163, 175, 0.5);
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border-radius: 4px;
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}
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.dark ::-webkit-scrollbar-thumb {
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background-color: rgba(75, 85, 99, 0.5);
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}
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.container {
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max-width: 1200px;
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margin: 0 auto;
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background: white;
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padding: 20px;
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border-radius: 10px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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}
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.upload-section {
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background: #f8f9fa;
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padding: 20px;
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border-radius: 8px;
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margin: 20px 0;
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text-align: center;
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}
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background: #0066cc;
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color: white;
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border: none;
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padding: 10px 20px;
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border-radius: 5px;
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cursor: pointer;
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transition: all 0.3s ease;
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font-size: 16px;
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}
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.button:hover {
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background: #0052a3;
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}
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.confidence-slider {
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width: 100%;
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max-width: 300px;
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margin: 20px auto;
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}
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input[type="file"] {
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margin-bottom: 10px;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="upload-section">
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<form action="/analyze" method="post" enctype="multipart/form-data">
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<div>
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| 170 |
+
<input type="file" name="file" accept="image/*" required>
|
| 171 |
+
</div>
|
| 172 |
+
<div class="confidence-slider">
|
| 173 |
+
<label for="threshold">Konfidenzschwelle: <span id="thresholdValue">0.60</span></label>
|
| 174 |
+
<input type="range" id="threshold" name="threshold"
|
| 175 |
+
min="0" max="1" step="0.05" value="0.60"
|
| 176 |
+
oninput="document.getElementById('thresholdValue').textContent = parseFloat(this.value).toFixed(2)">
|
| 177 |
+
</div>
|
| 178 |
<button type="submit" class="button">Analysieren</button>
|
| 179 |
</form>
|
| 180 |
</div>
|
|
|
|
| 184 |
"""
|
| 185 |
return content
|
| 186 |
|
|
|
|
| 187 |
@app.post("/analyze", response_class=HTMLResponse)
|
| 188 |
async def analyze_file(file: UploadFile = File(...)):
|
| 189 |
try:
|
|
|
|
| 190 |
contents = await file.read()
|
| 191 |
image = Image.open(io.BytesIO(contents))
|
| 192 |
|
|
|
|
| 195 |
predictions_locator = models["KnochenAuge"](image)
|
| 196 |
|
| 197 |
filtered_preds = [p for p in predictions_locator if p['score'] >= 0.6]
|
| 198 |
+
if filtered_preds:
|
| 199 |
+
result_image = draw_boxes(image, filtered_preds)
|
| 200 |
+
else:
|
| 201 |
+
result_image = image
|
| 202 |
+
|
| 203 |
result_image_b64 = image_to_base64(result_image)
|
| 204 |
|
| 205 |
results_html = """
|
|
|
|
| 208 |
<head>
|
| 209 |
<title>Ergebnisse</title>
|
| 210 |
<style>
|
|
|
|
|
|
|
| 211 |
body {
|
| 212 |
font-family: system-ui, -apple-system, sans-serif;
|
| 213 |
+
background: #f0f2f5;
|
| 214 |
margin: 0;
|
| 215 |
+
padding: 20px;
|
| 216 |
+
color: #1a1a1a;
|
| 217 |
}
|
| 218 |
|
| 219 |
+
/* Scrollbar personnalisée */
|
| 220 |
+
::-webkit-scrollbar {
|
| 221 |
+
width: 8px;
|
| 222 |
+
height: 8px;
|
| 223 |
+
}
|
| 224 |
+
|
| 225 |
+
::-webkit-scrollbar-track {
|
| 226 |
+
background: transparent;
|
| 227 |
+
}
|
| 228 |
+
|
| 229 |
+
::-webkit-scrollbar-thumb {
|
| 230 |
+
background-color: rgba(156, 163, 175, 0.5);
|
| 231 |
+
border-radius: 4px;
|
| 232 |
+
}
|
| 233 |
+
|
| 234 |
+
.dark ::-webkit-scrollbar-thumb {
|
| 235 |
+
background-color: rgba(75, 85, 99, 0.5);
|
| 236 |
}
|
| 237 |
|
| 238 |
.container {
|
| 239 |
+
max-width: 1200px;
|
| 240 |
+
margin: 0 auto;
|
| 241 |
+
background: white;
|
| 242 |
+
padding: 20px;
|
| 243 |
+
border-radius: 10px;
|
| 244 |
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
|
| 245 |
}
|
| 246 |
|
| 247 |
.results-grid {
|
| 248 |
display: grid;
|
| 249 |
+
grid-template-columns: 1fr 1fr;
|
| 250 |
+
gap: 20px;
|
| 251 |
+
margin-top: 20px;
|
| 252 |
}
|
| 253 |
|
| 254 |
.result-box {
|
| 255 |
+
background: #f8f9fa;
|
| 256 |
+
padding: 15px;
|
| 257 |
+
border-radius: 8px;
|
| 258 |
+
margin: 10px 0;
|
| 259 |
+
border: 1px solid #e9ecef;
|
| 260 |
}
|
| 261 |
|
| 262 |
+
.score-high { color: #0066cc; font-weight: bold; }
|
| 263 |
+
.score-medium { color: #ffa500; font-weight: bold; }
|
| 264 |
+
|
| 265 |
.back-button {
|
| 266 |
display: inline-block;
|
| 267 |
background: #0066cc;
|
| 268 |
color: white;
|
| 269 |
+
padding: 10px 20px;
|
| 270 |
+
border-radius: 5px;
|
| 271 |
text-decoration: none;
|
| 272 |
+
margin-top: 20px;
|
| 273 |
}
|
| 274 |
|
| 275 |
img {
|
| 276 |
max-width: 100%;
|
| 277 |
+
max-height: 60vh;
|
| 278 |
+
border-radius: 8px;
|
| 279 |
+
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
|
| 280 |
+
object-fit: contain;
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 281 |
}
|
| 282 |
</style>
|
| 283 |
</head>
|
| 284 |
<body>
|
| 285 |
<div class="container">
|
|
|
|
|
|
|
| 286 |
<div class="results-grid">
|
| 287 |
+
<div>
|
| 288 |
"""
|
| 289 |
|
| 290 |
# KnochenWächter results
|
| 291 |
+
results_html += "<h3>KnochenWächter</h3>"
|
| 292 |
for pred in predictions_watcher:
|
| 293 |
+
confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium"
|
| 294 |
+
results_html += f"""
|
| 295 |
+
<div class="result-box">
|
| 296 |
+
<span class="{confidence_class}">{pred['score']:.1%}</span> -
|
| 297 |
+
{translate_label(pred['label'])}
|
| 298 |
+
</div>
|
| 299 |
+
"""
|
| 300 |
|
| 301 |
# RöntgenMeister results
|
| 302 |
+
results_html += "<h3>RöntgenMeister</h3>"
|
| 303 |
for pred in predictions_master:
|
| 304 |
+
confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium"
|
| 305 |
+
results_html += f"""
|
| 306 |
+
<div class="result-box">
|
| 307 |
+
<span class="{confidence_class}">{pred['score']:.1%}</span> -
|
| 308 |
+
{translate_label(pred['label'])}
|
| 309 |
+
</div>
|
| 310 |
+
"""
|
| 311 |
|
|
|
|
| 312 |
results_html += f"""
|
| 313 |
+
</div>
|
| 314 |
+
<div>
|
| 315 |
<h3>Fraktur Lokalisation</h3>
|
| 316 |
<img src="{result_image_b64}" alt="Analyzed image">
|
| 317 |
</div>
|
|
|
|
| 332 |
<head>
|
| 333 |
<title>Fehler</title>
|
| 334 |
<style>
|
| 335 |
+
body {{
|
|
|
|
| 336 |
font-family: system-ui, -apple-system, sans-serif;
|
| 337 |
+
margin: 0;
|
| 338 |
+
padding: 20px;
|
| 339 |
+
}}
|
| 340 |
+
.back-button {{
|
| 341 |
+
display: inline-block;
|
| 342 |
+
background: #0066cc;
|
| 343 |
+
color: white;
|
| 344 |
+
padding: 10px 20px;
|
| 345 |
+
border-radius: 5px;
|
| 346 |
+
text-decoration: none;
|
| 347 |
+
margin-top: 20px;
|
| 348 |
}}
|
| 349 |
</style>
|
| 350 |
</head>
|
| 351 |
<body>
|
| 352 |
+
<h3>Fehler</h3>
|
| 353 |
<p>{str(e)}</p>
|
| 354 |
<a href="/" class="back-button">← Zurück</a>
|
| 355 |
</body>
|