Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+
+model = YOLO('ajeetsraina/clinical-dental-pathology-detector')
+
+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**"
+)
+
+demo.launch()
+```
+
+**Create `requirements.txt`:**
+```
+gradio
+ultralytics>=8.3.0
+pillow
+numpy
+torch
+torchvision
+opencv-python-headless