Chnages.

app.py

@@ -77,17 +77,6 @@ def generate_answers(img=None, aud = None, q = None, max_tokens = 30):
                                                   max_new_tokens=max_tokens,
                                                   return_dict_in_generate = True)
 
-    # for pos in range(max_tokens - 1):
-    #     model_output_logits = phi2_model.forward(inputs_embeds = combined_embeds)['logits']
-    #     print(model_output_logits.shape)
-    #     predicted_word_token_logits = model_output_logits[:, -1, :].unsqueeze(1)
-    #     predicted_word_token = torch.argmax(predicted_word_token_logits, dim = -1)
-    #     predicted_caption[:, pos] = predicted_word_token.view(1,-1).to('cpu')
-    #     print(predicted_caption)
-    #     next_token_embeds = phi2_model.model.embed_tokens(predicted_word_token)
-    #     combined_embeds   = torch.cat([combined_embeds, next_token_embeds], dim=1)
-    #     print("combined_embeds", combined_embeds.shape)
-    # predicted_captions_decoded = tokenizer.batch_decode(predicted_caption,ignore_index = 50256)[0]
     predicted_captions_decoded =tokenizer.batch_decode(predicted_caption.sequences[:, 1:])[0]
     predicted_captions_decoded = predicted_captions_decoded.replace("<|endoftext|>","")
     return predicted_captions_decoded