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from flask import Flask, request, jsonify, send_from_directory
# from flask_session import Session
from flask_cors import CORS # <-- New import here
from flask_cors import cross_origin
import openai
import os
from pytube import YouTube
import re
from langchain_openai.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from youtube_transcript_api import YouTubeTranscriptApi
from dotenv import load_dotenv
load_dotenv()
app = Flask(__name__, static_folder="./dist") # requests in the dist folder are being sent to http://localhost:5000/<endpoint>
CORS(app, resources={r"/*": {"origins": "*"}})
openai.api_key = os.environ["OPENAI_API_KEY"]
llm_name = "gpt-3.5-turbo"
qna_chain = None
@app.route('/', defaults={'path': ''})
@app.route('/<path:path>')
def serve(path):
if path != "" and os.path.exists(app.static_folder + '/' + path):
return send_from_directory(app.static_folder, path)
else:
return send_from_directory(app.static_folder, 'index.html')
def load_db(file, chain_type, k):
"""
Central Function that:
- Loads the database
- Creates the retriever
- Creates the chatbot chain
- Returns the chatbot chain
- A Dictionary containing
-- question
-- llm answer
-- chat history
-- source_documents
-- generated_question
s
Usage: question_answer_chain = load_db(file, chain_type, k)
response = question_answer_chain({"question": query, "chat_history": chat_history}})
"""
transcript = TextLoader(file).load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=70)
docs = text_splitter.split_documents(transcript)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(docs, embeddings)
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
# create a chatbot chain. Memory is managed externally.
qa = ConversationalRetrievalChain.from_llm(
llm = ChatOpenAI(temperature=0), #### Prompt Template is yet to be created
chain_type=chain_type,
retriever=retriever,
return_source_documents=True,
return_generated_question=True,
# memory=memory
)
return qa
def buffer(history, buff):
"""
Buffer the history.
Keeps only buff recent chats in the history
Usage: history = buffer(history, buff)
"""
if len(history) > buff :
print(len(history)>buff)
return history[-buff:]
return history
def is_valid_yt(link):
"""
Check if a link is a valid YouTube link.
Usage: boolean, video_id = is_valid_yt(youtube_string)
"""
pattern = r'^(?:https?:\/\/)?(?:www\.)?(?:youtube\.com\/watch\?v=|youtu\.be\/)([\w\-_]{11})(?:\S+)?$'
match = re.match(pattern, link)
if match:
return True, match.group(1)
else:
return False, None
def get_metadata(video_id) -> dict:
"""Get important video information.
Components are:
- title
- description
- thumbnail url,
- publish_date
- channel_author
- and more.
Usage: get_metadata(id)->dict
"""
try:
from pytube import YouTube
except ImportError:
raise ImportError(
"Could not import pytube python package. "
"Please install it with `pip install pytube`."
)
yt = YouTube(f"https://www.youtube.com/watch?v={video_id}")
video_info = {
"title": yt.title or "Unknown",
"description": yt.description or "Unknown",
"view_count": yt.views or 0,
"thumbnail_url": yt.thumbnail_url or "Unknown",
"publish_date": yt.publish_date.strftime("%Y-%m-%d %H:%M:%S")
if yt.publish_date
else "Unknown",
"length": yt.length or 0,
"author": yt.author or "Unknown",
}
return video_info
def save_transcript(video_id):
"""
Saves the transcript of a valid yt video to a text file.
"""
try:
transcript = YouTubeTranscriptApi.get_transcript(video_id)
except Exception as e:
print(f"Error fetching transcript for video {video_id}: {e}")
return None
if transcript:
with open('transcript.txt', 'w') as file:
for entry in transcript:
file.write(f"~{int(entry['start'])}~{entry['text']} ")
print(f"Transcript saved to: transcript.txt")
@app.route('/init', methods=['POST'])
@cross_origin()
def initialize():
"""
Initialize the qna_chain for a user.
"""
global qna_chain
qna_chain = 0
# NEED to authenticate the user here
yt_link = request.json.get('yt_link', '')
valid, id = is_valid_yt(yt_link)
if valid:
metadata = get_metadata(id)
try:
os.remove('./transcript.txt')
except:
print("No transcript file to remove.")
save_transcript(id)
# Initialize qna_chain for the user
qna_chain = load_db("./transcript.txt", 'stuff', 5)
# os.remove('./transcript.txt')
return jsonify({"status": "success",
"message": "qna_chain initialized.",
"metadata": metadata,
})
else:
return jsonify({"status": "error", "message": "Invalid YouTube link."})
@app.route('/response', methods=['POST'])
def response():
"""
- Expects youtube Video Link and chat-history in payload
- Returns response on the query.
"""
global qna_chain
req = request.get_json()
raw = req.get('chat_history', [])
# raw is a list of list containing two strings convert that into a list of tuples
if len(raw) > 0:
chat_history = [tuple(x) for x in raw]
else:
chat_history = []
# print(f"Chat History: {chat_history}")
memory = chat_history
query = req.get('query', '')
# print(f"Query: {query}")
if memory is None:
memory = []
if qna_chain is None:
return jsonify({"status": "error", "message": "qna_chain not initialized."}), 400
response = qna_chain({'question': query, 'chat_history': buffer(memory,7)})
if response['source_documents']:
pattern = r'~(\d+)~'
backlinked_docs = [response['source_documents'][i].page_content for i in range(len(response['source_documents']))]
timestamps = list(map(lambda s: int(re.search(pattern, s).group(1)) if re.search(pattern, s) else None, backlinked_docs))
return jsonify(dict(timestamps=timestamps, answer=response['answer']))
return jsonify(response['answer'])
@app.route('/transcript', methods=['POST'])
@cross_origin()
def send_transcript():
"""
Send the transcript of the video.
"""
try:
with open('transcript.txt', 'r') as file:
transcript = file.read()
return jsonify({"status": "success", "transcript": transcript})
except:
return jsonify({"status": "error", "message": "Transcript not found."})
if __name__ == '__main__':
app.run(debug=True)
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