|
import streamlit as st |
|
from transformers import pipeline |
|
from PIL import Image, ImageDraw |
|
|
|
st.set_page_config( |
|
page_title="Fraktur Detektion", |
|
layout="wide", |
|
initial_sidebar_state="collapsed" |
|
) |
|
|
|
st.markdown(""" |
|
<style> |
|
.stApp { |
|
padding: 0 !important; |
|
height: 100vh !important; |
|
overflow: hidden !important; |
|
} |
|
|
|
.block-container { |
|
padding: 0.25rem !important; |
|
max-width: 100% !important; |
|
} |
|
|
|
.stImage > img { |
|
width: 80% !important; |
|
height: auto !important; |
|
max-height: 200px !important; |
|
object-fit: contain !important; |
|
} |
|
|
|
h2, h3 { |
|
font-size: 0.9rem !important; |
|
} |
|
|
|
.result-box { |
|
font-size: 0.8rem !important; |
|
margin: 0.2rem 0 !important; |
|
} |
|
</style> |
|
""", unsafe_allow_html=True) |
|
|
|
@st.cache_resource |
|
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") |
|
} |
|
|
|
def translate_label(label): |
|
translations = { |
|
"fracture": "Knochenbruch", |
|
"no fracture": "Kein Bruch", |
|
"normal": "Normal", |
|
"abnormal": "Auffällig" |
|
} |
|
return translations.get(label.lower(), label) |
|
|
|
def draw_boxes(image, predictions): |
|
draw = ImageDraw.Draw(image) |
|
for pred in predictions: |
|
box = pred['box'] |
|
label = f"{translate_label(pred['label'])} ({pred['score']:.2%})" |
|
color = "#2563eb" if pred['score'] > 0.7 else "#eab308" |
|
|
|
draw.rectangle( |
|
[(box['xmin'], box['ymin']), (box['xmax'], box['ymax'])], |
|
outline=color, |
|
width=2 |
|
) |
|
|
|
|
|
text_bbox = draw.textbbox((box['xmin'], box['ymin']-15), label) |
|
draw.rectangle(text_bbox, fill=color) |
|
draw.text((box['xmin'], box['ymin']-15), label, fill="white") |
|
return image |
|
|
|
def main(): |
|
models = load_models() |
|
|
|
|
|
col1, col2 = st.columns([1, 2]) |
|
|
|
with col1: |
|
st.markdown("### 📤 Röntgenbild Upload") |
|
uploaded_file = st.file_uploader("", type=['png', 'jpg', 'jpeg']) |
|
|
|
if uploaded_file: |
|
conf_threshold = st.slider( |
|
"Konfidenzschwelle", |
|
min_value=0.0, max_value=1.0, |
|
value=0.60, step=0.05 |
|
) |
|
|
|
with col2: |
|
if uploaded_file: |
|
image = Image.open(uploaded_file) |
|
|
|
st.markdown("### 🔍 Meinung der KI-Experten") |
|
|
|
|
|
st.markdown("#### 👁️ Das KnochenAuge - Lokalisation") |
|
predictions = models["KnochenAuge"](image) |
|
filtered_preds = [p for p in predictions if p['score'] >= conf_threshold] |
|
|
|
if filtered_preds: |
|
result_image = image.copy() |
|
result_image = draw_boxes(result_image, filtered_preds) |
|
st.image(result_image, use_container_width=True) |
|
|
|
|
|
st.markdown("#### 🎯 KI-Analyse") |
|
col_left, col_right = st.columns(2) |
|
|
|
with col_left: |
|
st.markdown("**🛡️ Der KnochenWächter**") |
|
predictions = models["KnochenWächter"](image) |
|
for pred in predictions: |
|
score_color = "#22c55e" if pred['score'] > 0.7 else "#eab308" |
|
st.markdown(f""" |
|
<div class='result-box'> |
|
<span style='color: {score_color}; font-weight: 500;'> |
|
{pred['score']:.1%} |
|
</span> - {translate_label(pred['label'])} |
|
</div> |
|
""", unsafe_allow_html=True) |
|
|
|
with col_right: |
|
st.markdown("**🎓 Der RöntgenMeister**") |
|
predictions = models["RöntgenMeister"](image) |
|
for pred in predictions: |
|
score_color = "#22c55e" if pred['score'] > 0.7 else "#eab308" |
|
st.markdown(f""" |
|
<div class='result-box'> |
|
<span style='color: {score_color}; font-weight: 500;'> |
|
{pred['score']:.1%} |
|
</span> - {translate_label(pred['label'])} |
|
</div> |
|
""", unsafe_allow_html=True) |
|
else: |
|
st.info("Bitte laden Sie ein Röntgenbild hoch (JPEG, PNG)") |
|
|
|
if __name__ == "__main__": |
|
main() |
|
|