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| from youtube_transcript_api import YouTubeTranscriptApi | |
| from youtube_transcript_api.formatters import JSONFormatter | |
| import json | |
| import ingest | |
| import run_localGPT | |
| import utils | |
| def audio_to_transcript(video_id): | |
| sub = YouTubeTranscriptApi.get_transcript(video_id) | |
| formatted_subs = JSONFormatter().format_transcript(transcript=sub) | |
| with open("transcript.json", "w") as outfile: | |
| json.dump(sub, outfile) | |
| lstTexts = [] | |
| for dct in sub: | |
| lstTexts.append(dct['text']) | |
| strResult = ' '.join(lstTexts) | |
| with open('SOURCE_DOCUMENTS/transcript.txt', 'w') as outfile: | |
| outfile.write(strResult) | |
| transcript = ' '.join(lstTexts) | |
| utils.calculate_ends('transcript.json','transcript_end.json') | |
| utils.create_chunks('transcript_end.json','chunks.json') | |
| return transcript | |
| def start_training(): | |
| training_status = ingest.main() | |
| return training_status | |
| def replace_substring_and_following(input_string, substring): | |
| index = input_string.find(substring) | |
| if index != -1: | |
| return input_string[:index] | |
| else: | |
| return input_string | |
| def ask_question(strQuestion): | |
| answer = run_localGPT.main(device_type='cpu', strQuery=strQuestion) | |
| answer_cleaned = replace_substring_and_following(answer, "Unhelpful Answer") | |
| return answer_cleaned | |
| def summarize(): | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.chains.mapreduce import MapReduceChain | |
| from langchain.prompts import PromptTemplate | |
| model_id = "TheBloke/Llama-2-7B-Chat-GGML" | |
| model_basename = "llama-2-7b-chat.ggmlv3.q4_0.bin" | |
| llm = run_localGPT.load_model(device_type='cpu', model_id=model_id, model_basename=model_basename) | |
| text_splitter = CharacterTextSplitter() | |
| with open("SOURCE_DOCUMENTS/transcript.txt") as f: | |
| file_content = f.read() | |
| texts = text_splitter.split_text(file_content) | |
| from langchain.docstore.document import Document | |
| docs = [Document(page_content=t) for t in texts] | |
| from langchain.chains.summarize import load_summarize_chain | |
| chain = load_summarize_chain(llm, chain_type="map_reduce") | |
| summary = chain.run(docs) | |
| return summary | |