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import streamlit as st |
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from transformers import pipeline |
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from PIL import Image |
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import io |
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st.set_page_config(page_title="Knochenbrucherkennung", layout="centered") |
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@st.cache_resource |
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def load_model(): |
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return pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray") |
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def main(): |
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st.title("🦴 Knochenbrucherkennung") |
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st.write("Laden Sie ein Röntgenbild hoch.") |
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pipe = load_model() |
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uploaded_file = st.file_uploader( |
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"Röntgenbild auswählen", |
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type=['png', 'jpg', 'jpeg'] |
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) |
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conf_threshold = st.slider( |
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"Konfidenzschwelle", |
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min_value=0.0, |
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max_value=1.0, |
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value=0.3, |
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step=0.01 |
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) |
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if uploaded_file: |
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image = Image.open(uploaded_file) |
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max_size = (400, 400) |
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image.thumbnail(max_size, Image.Resampling.LANCZOS) |
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st.image(image, caption="Hochgeladenes Bild") |
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with st.spinner("Analyse läuft..."): |
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predictions = pipe(image) |
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st.subheader("Ergebnisse") |
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for pred in predictions: |
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if pred['score'] >= conf_threshold: |
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label = "Bruch erkannt" if "fracture" in pred['label'].lower() else "Kein Bruch" |
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st.write(f"• Diagnose: {label}") |
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st.write(f"• Konfidenz: {pred['score']:.2%}") |
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if "fracture" in pred['label'].lower() and pred['score'] >= conf_threshold: |
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st.warning("⚠️ Möglicher Knochenbruch erkannt!") |
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else: |
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st.success("✅ Kein Bruch erkannt") |
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if __name__ == "__main__": |
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main() |
