Update app.py

app.py

@@ -227,33 +227,48 @@ def analyze_symptoms(text):
         prediction = "No health condition detected"
         score = 0.0
 
+        # Handle all possible output types
         if result is None:
             logger.warning("Model output is None")
         elif isinstance(result, (str, int, float, bool)):
             logger.warning(f"Invalid model output type: {type(result)}, value: {result}")
-        elif isinstance(result, tuple):
-            logger.debug(f"Converting tuple to list: {result}")
-            result = list(result)
+        elif isinstance(result, (tuple, list)):
+            # Flatten nested tuples/lists
+            flattened = []
+            def flatten(item):
+                if isinstance(item, (tuple, list)):
+                    for subitem in item:
+                        flatten(subitem)
+                else:
+                    flattened.append(item)
+            flatten(result)
+            result = flattened
+            if not result:
+                logger.warning("Flattened model output is empty")
+            elif isinstance(result, list):
+                if not all(isinstance(item, dict) for item in result):
+                    logger.warning(f"Non-dictionary items in result: {result}")
+                elif not all("label" in item and "score" in item for item in result):
+                    logger.warning(f"Missing label or score in result: {result}")
+                else:
+                    prediction = result[0]["label"]
+                    score = result[0]["score"]
         elif isinstance(result, dict):
             logger.debug("Model returned single dictionary; wrapping in list")
             result = [result]
-        
-        if isinstance(result, list):
-            if len(result) == 0:
-                logger.warning("Model output is empty list")
-            elif not all(isinstance(item, dict) for item in result):
-                logger.warning(f"Non-dictionary items in result: {result}")
-            elif not all("label" in item and "score" in item for item in result):
-                logger.warning(f"Missing label or score in result: {result}")
-            else:
+            if "label" in result[0] and "score" in result[0]:
                 prediction = result[0]["label"]
                 score = result[0]["score"]
-                if not isinstance(prediction, str):
-                    logger.warning(f"Invalid label type: {type(prediction)}, value: {prediction}")
-                    prediction = "No health condition detected"
-                if not isinstance(score, (int, float)) or score < 0 or score > 1:
-                    logger.warning(f"Invalid score: {score}")
-                    score = 0.0
+            else:
+                logger.warning(f"Missing label or score in dictionary: {result}")
+
+        # Validate prediction and score
+        if not isinstance(prediction, str):
+            logger.warning(f"Invalid label type: {type(prediction)}, value: {prediction}")
+            prediction = "No health condition detected"
+        if not isinstance(score, (int, float)) or score < 0 or score > 1:
+            logger.warning(f"Invalid score: {score}")
+            score = 0.0
 
         if is_fallback_model:
             logger.warning("Using fallback DistilBERT model")