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import gradio as gr
from ultralytics import YOLO
from PIL import Image
from huggingface_hub import hf_hub_download
# Download model from Hugging Face
print("Downloading model from Hugging Face...")
model_path = hf_hub_download(
repo_id="ajeetsraina/clinical-dental-pathology-detector",
filename="models/dental_pathology_detector.pt"
)
print(f"Model downloaded to: {model_path}")
# Load model
model = YOLO(model_path)
def analyze_xray(image):
if image is None:
return None, "Please upload an X-ray image"
results = model.predict(image, conf=0.25)
annotated = results[0].plot()
annotated_pil = Image.fromarray(annotated)
detections = results[0].boxes
if len(detections) == 0:
report = "✅ No significant pathologies detected"
else:
class_counts = {}
for box in detections:
cls_name = model.names[int(box.cls[0])]
if cls_name not in class_counts:
class_counts[cls_name] = 0
class_counts[cls_name] += 1
report = f"**Total Findings:** {len(detections)}\n\n"
for cls_name, count in class_counts.items():
emoji = {"Cavity": "⚠️", "Fillings": "✅", "Implant": "🦷", "Impacted Tooth": "⚠️"}.get(cls_name, "ℹ️")
report += f"{emoji} **{cls_name}**: {count} detected\n"
return annotated_pil, report
demo = gr.Interface(
fn=analyze_xray,
inputs=gr.Image(type="pil", label="Upload Dental X-ray"),
outputs=[
gr.Image(type="pil", label="Analyzed X-ray"),
gr.Markdown(label="Clinical Report")
],
title="🦷 Clinical Dental Pathology Detector",
description="Upload dental X-ray to detect cavities, fillings, implants, and impacted teeth. **98.9% mAP@50 accuracy**",
article="Model: [ajeetsraina/clinical-dental-pathology-detector](https://huggingface.co/ajeetsraina/clinical-dental-pathology-detector)"
)
demo.launch()
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