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import streamlit as st
from gradio_client import Client
from llama_index.llms import Replicate
from llama_index.embeddings import LangchainEmbedding
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import set_global_service_context, ServiceContext, VectorStoreIndex, SimpleDirectoryReader
import os
# Ensure the environment variable is set
if "REPLICATE_API_TOKEN" not in os.environ:
raise ValueError("Please set the REPLICATE_API_TOKEN environment variable.")
else:
os.environ["REPLICATE_API_TOKEN"] = os.environ["REPLICATE_API_TOKEN"]
llm = Replicate(
model="replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b"
)
embeddings = LangchainEmbedding(
HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
)
service_context = ServiceContext.from_defaults(
chunk_size=1024,
llm=llm,
embed_model=embeddings
)
set_global_service_context(service_context)
# Transcribe function
def transcribe_video(youtube_url):
client = Client("https://sanchit-gandhi-whisper-jax.hf.space/")
result = client.predict(youtube_url, "transcribe", True, fn_index=7)
with open(f'{PATH}/docs.txt','w') as f:
f.write(result[1])
documents = SimpleDirectoryReader(PATH).load_data()
index = VectorStoreIndex.from_documents(documents)
return index.as_query_engine()
# Streamlit UI
st.title("YouTube Video Chatbot")
# Input for YouTube URL
youtube_url = st.text_input("Enter YouTube Video URL:")
if youtube_url and "query_engine" not in st.session_state:
st.write("Transcribing video... Please wait.")
st.session_state.query_engine = transcribe_video(youtube_url)
# Chatbot UI
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat history
for message in st.session_state.messages:
if message["role"] == "human":
st.write(f"You: {message['content']}")
else:
st.write(f"Chatbot: {message['content']}")
# User input
prompt = st.text_input("Ask something about the video:")
# React to user input
if prompt and "query_engine" in st.session_state:
# Add user message to chat history
st.session_state.messages.append({"role": "human", "content": prompt})
# Get response from the chatbot
response = st.session_state.query_engine.query(prompt)
response_text = response.response # Assuming the response has a 'response' attribute with the answer
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response_text})
# Refresh the page to show the updated chat history
if prompt:
st.experimental_rerun()
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