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app.py

@@ -4,6 +4,7 @@ import torch.nn.functional as F
 from torchvision import transforms, models
 from PIL import Image
 import json
+import traceback
 
 # Load model metadata
 CLASS_NAMES = [
@@ -23,7 +24,7 @@ DESCRIPTIONS = {
     "Crowding": "Insufficient space causing teeth to overlap or twist.",
     "Deepbite": "Upper front teeth excessively overlap lower front teeth.",
     "No Treatment Needed": "Teeth appear to be properly aligned.",
-    "Open Bite": "Upper and lower teeth don\'t touch when mouth is closed.",
+    "Open Bite": "Upper and lower teeth don't touch when mouth is closed.",
     "Overbite": "Upper front teeth protrude significantly over lower teeth.",
     "Spacing": "Gaps or spaces between teeth.",
     "Underbite": "Lower teeth protrude beyond upper teeth."
@@ -47,52 +48,56 @@ transform = transforms.Compose([
 
 def predict(image):
     """Predict orthodontic condition from image"""
-    if image is None:
-        return {"error": "No image provided"}
-    
-    # Convert to PIL if needed
-    if not isinstance(image, Image.Image):
-        image = Image.fromarray(image)
-    
-    image = image.convert("RGB")
-    
-    # Preprocess
-    img_tensor = transform(image).unsqueeze(0)
-    
-    # Predict
-    with torch.no_grad():
-        outputs = model(img_tensor)
-        probabilities = F.softmax(outputs, dim=1)[0]
-        confidence, predicted_idx = torch.max(probabilities, 0)
-    
-    predicted_condition = CLASS_NAMES[predicted_idx.item()]
-    confidence_pct = confidence.item() * 100
-    
-    # Get all probabilities
-    all_probs = {CLASS_NAMES[i]: float(probabilities[i].item() * 100) 
-                 for i in range(len(CLASS_NAMES))}
-    
-    # Determine recommendation
-    if predicted_condition == "No Treatment Needed":
-        recommendation = "not_candidate"
-        recommendation_text = "Based on the AI analysis, you may not need orthodontic treatment at this time."
-    elif confidence_pct >= 70:
-        recommendation = "candidate"
-        recommendation_text = f"You appear to be a good candidate for orthodontic treatment to address {predicted_condition.lower()}."
-    else:
-        recommendation = "requires_evaluation"
-        recommendation_text = "We recommend scheduling a consultation with an orthodontist for a thorough evaluation."
-    
-    return {
-        "predicted_condition": predicted_condition,
-        "confidence": round(confidence_pct, 2),
-        "all_probabilities": {k: round(v, 2) for k, v in all_probs.items()},
-        "recommendation": recommendation,
-        "recommendation_text": recommendation_text,
-        "condition_description": DESCRIPTIONS.get(predicted_condition, ""),
-        "model_version": "ResNet18_512x512",
-        "training_accuracy": 72.73
-    }
+    try:
+        if image is None:
+            return {"error": "No image provided"}
+        
+        # Convert to PIL if needed
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+        
+        image = image.convert("RGB")
+        
+        # Preprocess
+        img_tensor = transform(image).unsqueeze(0)
+        
+        # Predict
+        with torch.no_grad():
+            outputs = model(img_tensor)
+            probabilities = F.softmax(outputs, dim=1)[0]
+            confidence, predicted_idx = torch.max(probabilities, 0)
+        
+        predicted_condition = CLASS_NAMES[predicted_idx.item()]
+        confidence_pct = confidence.item() * 100
+        
+        # Get all probabilities
+        all_probs = {CLASS_NAMES[i]: float(probabilities[i].item() * 100) 
+                     for i in range(len(CLASS_NAMES))}
+        
+        # Determine recommendation
+        if predicted_condition == "No Treatment Needed":
+            recommendation = "not_candidate"
+            recommendation_text = "Based on the AI analysis, you may not need orthodontic treatment at this time."
+        elif confidence_pct >= 70:
+            recommendation = "candidate"
+            recommendation_text = f"You appear to be a good candidate for orthodontic treatment to address {predicted_condition.lower()}."
+        else:
+            recommendation = "requires_evaluation"
+            recommendation_text = "We recommend scheduling a consultation with an orthodontist for a thorough evaluation."
+        
+        return {
+            "predicted_condition": predicted_condition,
+            "confidence": round(confidence_pct, 2),
+            "all_probabilities": {k: round(v, 2) for k, v in all_probs.items()},
+            "recommendation": recommendation,
+            "recommendation_text": recommendation_text,
+            "condition_description": DESCRIPTIONS.get(predicted_condition, ""),
+            "model_version": "ResNet18_512x512",
+            "training_accuracy": 72.73
+        }
+    except Exception as e:
+        traceback.print_exc()
+        return {"error": str(e), "traceback": traceback.format_exc()}
 
 # Create Gradio interface
 demo = gr.Interface(
@@ -106,4 +111,4 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(show_error=True)