Create app.py

app.py

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+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')
+#OPENAI_KEY = "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"
+# https://pypi.org/project/youtube-transcript-api/
+
+
+
+_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 generate(video_url, question):
+  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"]
+
+examples = [['https://www.youtube.com/watch?v=u_P8md6brDI'],
+            ['https://www.youtube.com/watch?v=ao_OZ_bzMP8'] 
+           ]
+
+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, 
+                      examples = examples)
+demo.launch(share=False, debug=False)