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from models.whisper_model import AudioTranslator
from models.llm_model import LlmReasoner
class Chat:
def __init__(self, args) -> None:
self.args = args
def init_model(self):
print('\033[1;33m' + "Initializing models...".center(50, '-') + '\033[0m')
self.audio_translator = AudioTranslator(self.args)
self.llm_reasoner = LlmReasoner(self.args)
print('\033[1;32m' + "Model initialization finished!".center(50, '-') + '\033[0m')
def video2log(self, video_path):
audio_results = self.audio_translator(video_path)
en_log_result = []
en_log_result_tmp = ""
audio_transcript = self.audio_translator.match(audio_results)
en_log_result_tmp += f"\n{audio_transcript}"
en_log_result.append(en_log_result_tmp)
en_log_result = "\n\n".join(en_log_result)
print(f"\033[1;34mLog: \033[0m\n{en_log_result}\n")
return en_log_result
def chat2video(self, args, user_input, en_log_result):
self.llm_reasoner.create_qa_chain(args, en_log_result)
en_user_input = user_input
print("\n\033[1;32mGnerating response...\033[0m")
answer, generated_question, source_documents = self.llm_reasoner(en_user_input)
print(f"\033[1;32mQuestion: \033[0m{user_input}")
print(f"\033[1;32mAnswer: \033[0m{answer[0][1]}")
self.clean_history()
return answer, generated_question, source_documents
def clean_history(self):
self.llm_reasoner.clean_history()
return |