Update app.py

app.py

@@ -1,16 +1,28 @@
 import gradio as gr
 from ultralytics import YOLO
 from PIL import Image
+from huggingface_hub import hf_hub_download
 
-model = YOLO('ajeetsraina/clinical-dental-pathology-detector')
+# 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:
@@ -20,20 +32,38 @@ def analyze_xray(image):
             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")],
+    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**"
+    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()
+```
+
+---
 
+## **📝 Update requirements.txt**
 
+Also update your `requirements.txt`:
+```
+gradio
+ultralytics
+pillow
+torch
+opencv-python-headless
+huggingface_hub