Update app.py

app.py

@@ -75,10 +75,10 @@ def load_symptom_model():
         try:
             model = pipeline(
                 "text-classification",
-                model="albert-base-v2",
+                model="dmis-lab/biobert-base-cased-v1.2",
                 device=-1
             )
-            logger.warning("Fallback to albert-base-v2 model")
+            logger.warning("Fallback to biobert-base-cased-v1.2 model")
             return model
         except Exception as fallback_e:
             logger.error(f"Fallback model failed: {str(fallback_e)}")
@@ -185,25 +185,28 @@ def analyze_symptoms(text):
             result = symptom_classifier(text)
         logger.debug(f"Model output: {result}")
         
-        # Handle unexpected output structures
-        if not result:
-            logger.warning("Model output is empty")
+        # Handle all possible output structures
+        if result is None or isinstance(result, (str, int, float)):
+            logger.warning(f"Invalid model output: {result}")
             return "No health condition detected", 0.0
         if isinstance(result, tuple):
-            logger.warning(f"Model returned tuple: {result}")
-            result = list(result) if len(result) > 0 else []
+            logger.debug(f"Converting tuple to list: {result}")
+            result = list(result)
         if not isinstance(result, list) or len(result) == 0:
             logger.warning(f"Invalid model output: {result}")
             return "No health condition detected", 0.0
-        if not isinstance(result[0], dict) or not all(k in result[0] for k in ["label", "score"]):
+        if not isinstance(result[0], dict):
             logger.warning(f"Invalid result structure: {result[0]}")
             return "No health condition detected", 0.0
+        if not all(k in result[0] for k in ["label", "score"]):
+            logger.warning(f"Missing label or score in result: {result[0]}")
+            return "No health condition detected", 0.0
         
         prediction = result[0]["label"]
         score = result[0]["score"]
         if is_fallback_model:
-            logger.warning("Using fallback model")
-            prediction = f"{prediction} (fallback)"
+            logger.warning("Using fallback BioBERT model")
+            prediction = f"{prediction} (biobert)"
         logger.info(f"Prediction: {prediction}, Score: {score:.4f}")
         return prediction, score
     except Exception as e:
@@ -249,7 +252,7 @@ def analyze_voice(audio_file):
         
         unique_path = os.path.join(
             temp_dir,
-            f"audio_{datetime.utcnow().strftime('%Y%m%d%H%M%S%f')}_{os.path.basename(audio_file)}"
+            f"audio_{datetime.utcnow().strftime('%Y%m%d%H%M%S%f')}_{os.path.basename(audio_file or 'unknown.wav')}"
         )
         try:
             shutil.copy(audio_file, unique_path)