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import streamlit as st | |
import logging | |
import asyncio | |
from contextlib import asynccontextmanager | |
from app import QueryRequest # Import the request model | |
# Configure logging | |
logging.basicConfig(level=logging.INFO) | |
logger = logging.getLogger(__name__) | |
# Set page config | |
st.set_page_config(page_title="Certification Chat", layout="centered") | |
st.title("🎓 Certification Chat Assistant") | |
# Create a function to handle the async call | |
async def async_query(query_text): | |
from app import handle_query # Import here to avoid circular imports | |
request = QueryRequest(query=query_text) | |
return await handle_query(request) | |
# Function to run async code in Streamlit | |
def run_async(coroutine): | |
try: | |
loop = asyncio.get_event_loop() | |
except RuntimeError: | |
loop = asyncio.new_event_loop() | |
asyncio.set_event_loop(loop) | |
return loop.run_until_complete(coroutine) | |
# User input | |
user_input = st.text_area("💬 Enter your prompt:", height=150) | |
if user_input: | |
st.markdown("## 🧠 Response") | |
try: | |
# Use try-except to handle errors | |
with st.spinner("Processing your query..."): | |
# Run the async function | |
result = run_async(async_query(user_input)) | |
# Display output | |
st.write("**Certification:**", result["certification"]) | |
st.write("**Answer from '.' chunking method:**", result["certif_index"]) | |
st.write("**Answer from hybrid chunking method:**", result["certification_index"]) | |
except Exception as e: | |
st.error(f"An error occurred: {str(e)}") | |
logger.error(f"Error processing query: {e}", exc_info=True) |