Delete app11_03.py

app11_03.py

@@ -1,113 +0,0 @@
-import pickle
-
-from youtube_transcript_api import YouTubeTranscriptApi
-from dotenv import load_dotenv
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores.faiss import FAISS
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.chains import ChatVectorDBChain
-from langchain.prompts import PromptTemplate
-from pathlib import Path
-import os
-import openai
-import gradio as gr 
-
-load_dotenv()
-OPENAI_KEY = os.getenv('OPENAI_KEY')
-
-
-
-
-_template = """ Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
-Chat History:
-{chat_history}
-Follow Up Input: {question}
-Standalone question:"""
-CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-template = """You are an AI version of the youtuber {name} .
-You are given the following extracted parts of a long document and a question. Provide a conversational answer.
-Question: {question}
-=========
-{context}
-=========
-Answer:"""
-QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context", "name"])
-
-video1 = "ReeLQR7KCcM"
-youtuberName = ""
-
-def gpt_api (input_text):
-    completion = openai.Completion.create(
-    engine="text-davinci-003",
-    prompt=input_text,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0,
-    max_tokens=300,
-    n=1,
-    stop="",
-    temperature=0.6,
-    )
-    response = completion.choices[0].text.strip()
-    return response
-
-def generate(video_url, question):
-  if (video_url ==""): return ""
-  if "youtube.com/watch?v=" in video_url: x=111
-  else: return "Неверный URL"
-
-  video_id = video_url[-11:]
-  try:
-    t = YouTubeTranscriptApi.get_transcript(video_id,languages=["en"])
-    # do something with the transcript
-  except Exception as e:
-    return "An error occurred:"+e
-
-  finalString = ""
-  for item in t:
-      text = item['text']
-      finalString += text + " "
-  print("Transcript:",finalString)
-  print("Transcript lenght:",len(finalString))
-  if (len(finalString)>15000): finalString = finalString[:15000]
-
-  # load data sources to text (yt->text)
-  text_splitter = CharacterTextSplitter()
-  chunks = text_splitter.split_text(finalString)
-  vectorStorePkl = Path("vectorstore.pkl")
-  vectorStore = None
-  # if vectorStorePkl.is_file():
-  #     print("vector index found.. ")
-  #     with open('vectorstore.pkl', 'rb') as f:
-  #         vectorStore = pickle.load(f)
-  # else:
-  print("regenerating search index vector store..")
-  # It uses OpenAI API to create embeddings (i.e. a feature vector)
-  # https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture
-  vectorStore = FAISS.from_texts(chunks, OpenAIEmbeddings(openai_api_key=OPENAI_KEY))
-  with open("vectorstore.pkl", "wb") as f:
-      pickle.dump(vectorStore, f)
-
-  qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0, openai_api_key=OPENAI_KEY),
-                                  vectorstore=vectorStore, qa_prompt=QA_PROMPT)
-
-  chat_history = []
-  userInput = question 
-
-  response = qa({"name": youtuberName, "question": userInput, "chat_history": chat_history}, return_only_outputs=True)
-  print("Result:",response["answer"])
-  return response["answer"]
-#======================================
-
-
-title = "YouTube Summorize (only english video < 15 min)"
-demo = gr.Interface(fn=generate, css=".gradio-container {background-color: lightblue}",
-                     inputs=[
-                              gr.Textbox(lines=1, label="Video URL"), 
-                              gr.Textbox(lines=1, label="Question", value="What is this video about?"),
-                              ], 
-                      outputs=[gr.Textbox(lines=4, label="Ответ:")], 
-                      title = title)
-demo.launch(share=False, debug=True)