Update app.py

app.py

@@ -81,23 +81,6 @@ def add_note_to_history(note, note_history):
     return [note_history]
 
 
-def chat(message, history):
-    history = history or []
-    if history: 
-        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
-    else:
-        history_useful = []
-    history_useful = add_note_to_history(message, history_useful)
-    inputs = tokenizer(history_useful, return_tensors="pt")
-    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
-    reply_ids = model.generate(**inputs)
-    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
-    history_useful = add_note_to_history(response, history_useful)
-    list_history = history_useful[0].split('</s> <s>')
-    history.append((list_history[-2], list_history[-1]))
-    ret = store_message(message, response) # Save to dataset - uncomment if you uncomment above to save inputs and outputs to your dataset
-    return history, history, ret
-    
 
 SAMPLE_RATE = 16000
 model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
@@ -123,9 +106,9 @@ def transcribe(audio, state = ""):
         if type(transcriptions) == tuple and len(transcriptions) == 2:
             transcriptions = transcriptions[0]
         transcriptions = transcriptions[0]
-    store_message(transcriptions, state) # Save to dataset - uncomment to store into a dataset - hint you will need your HF_TOKEN
+    ret = store_message(transcriptions, state) # Save to dataset - uncomment to store into a dataset - hint you will need your HF_TOKEN
     state = state + transcriptions + " "
-    return state, state
+    return state, state, ret
 
 gr.Interface(
     fn=transcribe,