Update app.py

app.py

@@ -43,29 +43,27 @@ def parse(wav_file):
 
 
 # Function to retrieve an answer based on a question (using fuzzy matching)
-# def get_answer(wav_file=None, text=None):
+def get_answer(wav_file=None):
     
-#     if type(wav_file) != 'str' or type(text != 'str'):
-#         input_values = read_file_and_process(wav_file)
-#         with torch.no_grad():
-#             logits = model(**input_values).logits
-#         user_question = parse_transcription(logits)
-#     else:
-#         user_question = wav_file
-        
-#     highest_score = 0
-#     best_answer = None
-
-#     for item in DATASET:
-#         similarity_score = fuzz.token_set_ratio(user_question, item["question"])
-#         if similarity_score > highest_score:
-#             highest_score = similarity_score
-#             best_answer = item["answer"]
-
-#     if highest_score >= 80:  # Adjust the similarity threshold as needed
-#         return best_answer
-#     else:
-#         return "I don't have an answer to that question."
+    input_values = read_file_and_process(wav_file)
+    
+    with torch.no_grad():
+        logits = model(**input_values).logits
+    user_question = parse_transcription(logits)
+    
+    highest_score = 0
+    best_answer = None
+
+    for item in DATASET:
+        similarity_score = fuzz.token_set_ratio(user_question, item["question"])
+        if similarity_score > highest_score:
+            highest_score = similarity_score
+            best_answer = item["answer"]
+
+    if highest_score >= 80:  # Adjust the similarity threshold as needed
+        return best_answer
+    else:
+        return "I don't have an answer to that question."
 
 
 model_id = "jonatasgrosman/wav2vec2-large-xlsr-53-persian"