Create helperbot_bigdl.py

helperbot_bigdl.py

@@ -0,0 +1,46 @@
+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