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Update app.py
Browse files
app.py
CHANGED
@@ -2,9 +2,7 @@ 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 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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@@ -69,12 +67,7 @@ def draw_boxes(image, predictions):
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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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@@ -84,121 +77,116 @@ def image_to_base64(image):
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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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COMMON_STYLES = """
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:root {
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color-scheme: light dark;
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}
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@media (prefers-color-scheme: dark) {
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body {
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background: #1a1a1a !important;
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color: #ffffff !important;
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}
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.container {
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background: #2d2d2d !important;
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}
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.result-box, .upload-section {
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background: #363636 !important;
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border-color: #555 !important;
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}
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.button {
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background: #0066cc !important;
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}
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input[type="file"]::file-selector-button {
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background: #444 !important;
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color: #fff !important;
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border-color: #555 !important;
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}
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input[type="range"] {
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background: #444 !important;
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}
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.button-progress {
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background: rgba(255, 255, 255, 0.2) !important;
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}
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}
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body {
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font-family: system-ui, -apple-system, sans-serif;
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background: #
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margin: 0;
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padding: 20px;
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color: #
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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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@media (prefers-color-scheme: dark) {
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::-webkit-scrollbar-thumb {
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background-color: rgba(75, 85, 99, 0.5);
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}
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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:
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padding: 20px;
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border-radius: 10px;
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box-shadow: 0
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}
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.button {
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background: #
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color: white;
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border: none;
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padding: 12px 30px;
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border-radius: 8px;
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cursor: pointer;
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font-size: 1.1em;
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position: relative;
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}
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.button:
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background: #
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}
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}
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100% { width: 100%; }
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}
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width: 0;
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}
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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: 8px;
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}
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"""
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@@ -210,86 +198,15 @@ async def main():
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<head>
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<title>Fraktur Detektion</title>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<style>
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{COMMON_STYLES}
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.upload-section {{
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background: #f8f9fa;
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padding: 40px;
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border-radius: 12px;
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margin: 20px 0;
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text-align: center;
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border: 2px dashed #ccc;
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transition: all 0.3s ease;
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}}
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.upload-section:hover {{
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border-color: #0066cc;
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}}
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input[type="file"] {{
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font-size: 1.1em;
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margin: 20px 0;
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}}
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input[type="file"]::file-selector-button {{
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font-size: 1em;
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padding: 10px 20px;
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border-radius: 8px;
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border: 1px solid #ccc;
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background: #f8f9fa;
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transition: all 0.3s ease;
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cursor: pointer;
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}}
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input[type="file"]::file-selector-button:hover {{
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background: #e9ecef;
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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="range"] {{
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width: 100%;
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height: 8px;
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border-radius: 4px;
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background: #e9ecef;
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outline: none;
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transition: all 0.3s ease;
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-webkit-appearance: none;
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}}
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input[type="range"]::-webkit-slider-thumb {{
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-webkit-appearance: none;
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width: 20px;
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height: 20px;
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border-radius: 50%;
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background: #0066cc;
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cursor: pointer;
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border: none;
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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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<
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</
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<div class="confidence-slider">
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<label for="threshold">Konfidenzschwelle: <span id="thresholdValue">0.60</span></label>
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<input type="range" id="threshold" name="threshold"
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min="0" max="1" step="0.05" value="0.60"
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oninput="document.getElementById('thresholdValue').textContent = parseFloat(this.value).toFixed(2)">
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</div>
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<button type="submit" class="button">
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Analysieren
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<div class="button-progress"></div>
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</button>
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</form>
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</div>
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</div>
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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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if filtered_preds
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result_image = draw_boxes(image, filtered_preds)
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else:
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result_image = image
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result_image_b64 = image_to_base64(result_image)
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results_html = f"""
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@@ -322,57 +235,7 @@ async def analyze_file(file: UploadFile = File(...)):
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<head>
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<title>Ergebnisse</title>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<style>
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{COMMON_STYLES}
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.results-grid {{
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display: grid;
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grid-template-columns: 1fr 1fr;
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gap: 20px;
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margin-top: 20px;
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}}
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.result-box {{
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background: #f8f9fa;
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padding: 20px;
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border-radius: 12px;
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margin: 10px 0;
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border: 1px solid #e9ecef;
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}}
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.score-high {{
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color: #0066cc;
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font-weight: bold;
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}}
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.score-medium {{
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color: #ffa500;
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font-weight: bold;
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}}
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.back-button {{
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display: inline-block;
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text-decoration: none;
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margin-top: 20px;
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}}
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h3 {{
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color: #0066cc;
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margin-top: 0;
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}}
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@media (prefers-color-scheme: dark) {{
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h3 {{
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color: #66b3ff;
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}}
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}}
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@media (max-width: 768px) {{
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.results-grid {{
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grid-template-columns: 1fr;
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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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</div>
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</div>
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<a href="/" class="button
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← Zurück
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<div class="button-progress"></div>
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</a>
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</div>
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</body>
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</html>
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<head>
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<title>Fehler</title>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<style>
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{COMMON_STYLES}
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.error-box {{
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background: #fee2e2;
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border: 1px solid #ef4444;
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padding: 20px;
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border-radius: 8px;
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margin: 20px 0;
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}}
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@media (prefers-color-scheme: dark) {{
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.error-box {{
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background: #471818;
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border-color: #dc2626;
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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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<div class="
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<h3>Fehler</h3>
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<p>{str(e)}</p>
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</div>
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<a href="/" class="button
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← Zurück
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<div class="button-progress"></div>
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</a>
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</div>
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</body>
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</html>
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"""
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=7860)
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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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app = FastAPI()
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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((box['xmin'], box['ymin']-20), label, fill=(255, 255, 255, 255))
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return result_image
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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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STYLES = """
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body {
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font-family: system-ui, -apple-system, sans-serif;
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background: #2d3748;
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margin: 0;
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padding: 20px;
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color: #e2e8f0;
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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: #1a202c;
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padding: 20px;
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border-radius: 10px;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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}
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.upload-section {
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background: #2d3748;
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padding: 40px;
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border-radius: 12px;
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margin: 20px 0;
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text-align: center;
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border: 2px dashed #4a5568;
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}
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/* Animation de chargement */
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@keyframes loading {
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0% { background-position: 200% 0; }
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100% { background-position: -200% 0; }
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}
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.button {
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background: #4299e1;
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color: white;
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border: none;
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padding: 12px 30px;
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border-radius: 8px;
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cursor: pointer;
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font-size: 1.1em;
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overflow: hidden;
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}
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.button:active {
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background: linear-gradient(90deg, #4299e1 0%, #63b3ed 50%, #4299e1 100%);
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background-size: 200% 100%;
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animation: loading 2s infinite linear;
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}
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input[type="file"] {
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font-size: 1.1em;
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margin: 20px 0;
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color: #e2e8f0;
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}
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input[type="file"]::file-selector-button {
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font-size: 1em;
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padding: 10px 20px;
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border-radius: 8px;
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border: 1px solid #4a5568;
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background: #2d3748;
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color: #e2e8f0;
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cursor: pointer;
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}
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input[type="file"]::file-selector-button:hover {
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background: #4a5568;
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}
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.result-box {
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background: #2d3748;
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padding: 20px;
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border-radius: 12px;
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margin: 10px 0;
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border: 1px solid #4a5568;
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}
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.score-high {
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color: #48bb78;
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font-weight: bold;
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}
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.score-medium {
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color: #ed8936;
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font-weight: bold;
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}
|
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|
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img {
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max-width: 100%;
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border-radius: 8px;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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}
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h3 {
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color: #63b3ed;
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margin-top: 0;
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}
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.results-grid {
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display: grid;
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grid-template-columns: 1fr 1fr;
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gap: 20px;
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margin-top: 20px;
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}
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@media (max-width: 768px) {
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.results-grid {
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grid-template-columns: 1fr;
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}
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}
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"""
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<head>
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<title>Fraktur Detektion</title>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<style>{STYLES}</style>
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|
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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">
|
207 |
+
<input type="file" name="file" accept="image/*" required>
|
208 |
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<br>
|
209 |
+
<button type="submit" class="button">Analysieren</button>
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|
210 |
</form>
|
211 |
</div>
|
212 |
</div>
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|
226 |
predictions_locator = models["KnochenAuge"](image)
|
227 |
|
228 |
filtered_preds = [p for p in predictions_locator if p['score'] >= 0.6]
|
229 |
+
result_image = draw_boxes(image, filtered_preds) if filtered_preds else image
|
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|
230 |
result_image_b64 = image_to_base64(result_image)
|
231 |
|
232 |
results_html = f"""
|
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|
235 |
<head>
|
236 |
<title>Ergebnisse</title>
|
237 |
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
238 |
+
<style>{STYLES}</style>
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|
239 |
</head>
|
240 |
<body>
|
241 |
<div class="container">
|
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|
275 |
</div>
|
276 |
</div>
|
277 |
|
278 |
+
<a href="/" class="button">← Zurück</a>
|
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|
279 |
</div>
|
280 |
</body>
|
281 |
</html>
|
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|
290 |
<head>
|
291 |
<title>Fehler</title>
|
292 |
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
293 |
+
<style>{STYLES}</style>
|
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|
294 |
</head>
|
295 |
<body>
|
296 |
<div class="container">
|
297 |
+
<div class="result-box">
|
298 |
<h3>Fehler</h3>
|
299 |
<p>{str(e)}</p>
|
300 |
</div>
|
301 |
+
<a href="/" class="button">← Zurück</a>
|
|
|
|
|
|
|
302 |
</div>
|
303 |
</body>
|
304 |
</html>
|
305 |
"""
|
306 |
|
307 |
if __name__ == "__main__":
|
308 |
+
import uvicorn
|
309 |
uvicorn.run(app, host="0.0.0.0", port=7860)
|