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| from bs4 import BeautifulSoup | |
| import requests | |
| from requests.auth import HTTPBasicAuth | |
| import run_localGPT | |
| 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 transcript(page_id): | |
| url = f"https://srikanthnm.atlassian.net/wiki/rest/api/content/{page_id}?expand=body.storage" # Replace with the actual URL you want to access | |
| username = "srikanth.nm@gmail.com" | |
| password = "ATATT3xFfGF09rugcjiT06v8xMayt5ggayMNiwz4b6w07PWQxPvpi4fMDzwwHxKt-v8dGx49uiulIMKHUUYroeS8cXvMKYfi7sQnFsYNfGslPVqSq1BQrzPhTio-xmYOHcit5ijzU9cSGGa7eLXUMxQTsSQjLhtZ-EQPI8h6aki690_-evLFZmU=3910FFD4" | |
| response = requests.get(url, auth=HTTPBasicAuth(username, password)) | |
| # Check if the request was successful (status code 200) | |
| if response.status_code == 200: | |
| # Process the response data (if applicable) | |
| data = response.json() | |
| else: | |
| data = f"Error: Unable to access the URL. Status code: {response.status_code}" | |
| soup = BeautifulSoup(data['body']['storage']['value'],"html.parser") | |
| page_content = soup.get_text() | |
| page_content_cleaned = page_content.replace('\xa0',' ') | |
| page_content_cleaned | |
| with open('SOURCE_DOCUMENTS/confluence.txt', 'w') as outfile: | |
| outfile.write(page_content_cleaned[:1998]) | |
| return page_content_cleaned[:1998] | |
| def summarize(): | |
| from langchain import PromptTemplate, LLMChain | |
| 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/confluence.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 |