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import streamlit as st
from src.pipeline import QAPipeline
from langchain_community.document_loaders import CSVLoader
import os
import tempfile
import asyncio
import nest_asyncio
nest_asyncio.apply()
os.environ["STREAMLIT_WATCHER_IGNORE_PATTERNS"] = "*/torch/*"
pipeline = QAPipeline()
st.title("Recipe Q&A")
# File upload section
st.header("Upload CSV")
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
if uploaded_file is not None:
if st.button("Add Documents"):
with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_path = tmp_file.name
loader = CSVLoader(file_path=tmp_path)
data = loader.load()
with st.spinner("Uploading documents..."):
asyncio.run(pipeline.upload_documents(data))
os.remove(tmp_path)
st.success("Documents uploaded successfully.")
# Query section
st.header("Ask a Question")
query = st.text_input("Enter your question:")
if "response" not in st.session_state:
st.session_state.response = None
# Get answer
if st.button("Get Answer") and query:
with st.spinner("Getting answer..."):
response = asyncio.run(pipeline.answer_query_(query))
st.session_state.response = response
st.write("**Answer:**")
st.write(response.answer)
if st.session_state.response:
if st.session_state.response.web_search:
if st.button("Search the web for this?"):
with st.spinner("Searching web..."):
web_response = asyncio.run(pipeline.search_web(query))
if web_response:
st.write("**Web Search Result:**")
st.write(web_response)
else:
st.write("No web search result found.")
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