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import os
import streamlit as st
from llama_index.core import VectorStoreIndex, download_loader, ServiceContext
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index.llms import Gemini, HuggingFaceInferenceAPI, OpenAI
# Create Streamlit web app
def main():
st.title("Webpage Querier by Rahul Bhoyar")
# Sidebar for customizations
with st.sidebar:
st.subheader("Customize Settings")
loader = download_loader("BeautifulSoupWebReader")()
hf_token = st.text_input("Enter your Hugging Face token:")
llm = HuggingFaceInferenceAPI(model_name="HuggingFaceH4/zephyr-7b-alpha", token=hf_token)
# Main content area
st.markdown("Query your Web page data with using this chatbot")
# User input: Web page link
url = st.text_input("Enter the URL of the web page:")
# Create Service Context
embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
# Load documents
if url:
documents = loader.load_data(urls=[url])
st.success("Documents loaded successfully!")
with st.spinner('Creating Vector Embeddings...'):
# Create Vector Store Index
index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
# Persist Storage Context
index.storage_context.persist()
# Create Query Engine
query_engine = index.as_query_engine()
# User input: Query
query = st.text_input("Ask a question:")
if query:
# Run Query
response = query_engine.query(query)
# Display Result
st.markdown(f"**Response:** {response}")
else:
st.warning("Please enter a valid URL.")
if __name__ == "__main__":
main()
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