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import gradio as gr
from langchain_community.document_loaders import YoutubeLoader
from langchain_cohere import ChatCohere
from langchain import hub
from langchain_chroma import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_cohere import CohereEmbeddings
import os
import os
COHERE_API_KEY = os.environ.get("COHERE_API_KEY")
llm = ChatCohere(model="command-r",cohere_api_key=COHERE_API_KEY)
prompt = hub.pull("rlm/rag-prompt")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# Function to load YouTube details
def get_youtube_details(video_url):
print(video_url)
loader = YoutubeLoader.from_youtube_url(str(video_url), add_video_info=False)
docs = loader.load()
print(docs)
print("Video transcripts loaded in DB")
return docs, loader
# Function to handle user messages and update the history
def user_message(message, history):
return "", history + [[message, None]]
# Function to clear the vector store (optional, not used in this example)
def clear_vectorstore(vectorstore):
vectorstore.delete_all()
return "Vector store cleared."
# Function to clear the text box and reset the state
def clear_textbox():
return "", None, None
# Function to handle bot responses
def bot_message(history, docs):
if docs is None:
return history
print(docs,"instide bot_message")
user_question = history[-1][0]
splits = text_splitter.split_documents(docs)
print(splits,"splits are also here")
vectorstore = Chroma.from_documents(documents=splits, embedding=CohereEmbeddings(model="embed-english-light-v3.0",
cohere_api_key=COHERE_API_KEY))
retriever = vectorstore.as_retriever()
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
response = rag_chain.invoke(user_question)
history[-1][1] = response
return history
title=(
"""
<center>
<h1> VideoQ: Quick Answers, Skip Clickbait </h1>
<b> text 📧<b>
</center>
"""
)
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
# gr.Markdown("# VideoQ: Quick Answers, Skip Clickbait")
with gr.Row():
gr.HTML(title,label=" ")
gr.Markdown("""
### Skip the endless scrolling. VideoQ provides instant video insights.
### Ask Questions to YouTube video and Save Time
""",label="Description")
text_box = gr.Textbox(lines=2, placeholder="Enter link of the YouTube video",label="Youtube valid link")
with gr.Row():
load_button = gr.Button("Load Document")
clear_button = gr.Button("Clear Document")
docs_box = gr.State()
loader_box = gr.State()
load_button.click(fn=get_youtube_details, inputs=[text_box], outputs=[docs_box, loader_box])
clear_button.click(fn=clear_textbox, inputs=[], outputs=[text_box, docs_box, loader_box])
chatbot_interface = gr.Chatbot(show_copy_button=True,label=" ")
msg = gr.Textbox(label="Message")
with gr.Row():
submit_btn = gr.Button("Submit")
clear_btn = gr.Button("Clear")
submit_btn.click(user_message, [msg, chatbot_interface], [msg, chatbot_interface], queue=False).then(
bot_message, [chatbot_interface, docs_box], chatbot_interface)
msg.submit(user_message, [msg, chatbot_interface], [msg, chatbot_interface], queue=False).then(
bot_message, [chatbot_interface, docs_box], chatbot_interface)
clear_btn.click(lambda: None, None, chatbot_interface, queue=False)
demo.launch(server_name="0.0.0.0", server_port=7860)
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