Spaces:
Sleeping
Sleeping
| import asyncio | |
| import streamlit as st | |
| import tempfile | |
| import sys | |
| import os | |
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) | |
| from agents.pdf_agent import PDFAgent | |
| from agents.weather_agent import WeatherAgent | |
| # Ensure an event loop exists for async libraries (fix for Google Generative AI Embeddings) | |
| try: | |
| asyncio.get_event_loop() | |
| except RuntimeError: | |
| asyncio.set_event_loop(asyncio.new_event_loop()) | |
| st.set_page_config(page_title="LangGraph Agents Demo", layout="wide") | |
| st.title("LangGraph Agents Demo") | |
| tab1, tab2, tab3 = st.tabs(["PDF Agent", "Weather Agent", "Multi-Agent QA"]) | |
| with tab1: | |
| st.header("PDF Agent") | |
| uploaded_pdf = st.file_uploader("Upload a PDF", type=["pdf"]) | |
| question = st.text_input("Ask a question about the PDF:") | |
| if uploaded_pdf: | |
| st.info(f"PDF uploaded: {uploaded_pdf.name}, size: {uploaded_pdf.size} bytes") | |
| if uploaded_pdf and question: | |
| try: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file: | |
| tmp_file.write(uploaded_pdf.read()) | |
| tmp_path = tmp_file.name | |
| st.info(f"Saved PDF to temp file: {tmp_path}") | |
| pdf_agent = PDFAgent(pdf_path=tmp_path) | |
| with st.spinner("Processing..."): | |
| answer = pdf_agent.ask(question) | |
| st.success("Answer:") | |
| st.write(answer) | |
| except Exception as e: | |
| st.error(f"Error processing PDF: {e}") | |
| import traceback | |
| st.text(traceback.format_exc()) | |
| with tab2: | |
| st.header("Weather Agent") | |
| location = st.text_input("Enter a location for weather info: e.g. Mumbai") | |
| if location: | |
| weather_agent = WeatherAgent() | |
| with st.spinner("Fetching weather..."): | |
| try: | |
| result = weather_agent.ask(location) | |
| st.success("Weather Info:") | |
| st.write(result) # This might be None or a dict | |
| # Try to extract the answer if it's a dict or object | |
| # if isinstance(result, dict): | |
| # # Try common keys | |
| # if "output" in result: | |
| # st.write(result["output"]) | |
| # elif "result" in result: | |
| # st.write(result["result"]) | |
| # else: | |
| # st.write(str(result)) | |
| # elif hasattr(result, "content"): | |
| # st.write(result.content) | |
| # elif result is not None: | |
| # st.write(str(result)) | |
| except Exception as e: | |
| st.error(f"Error: {e}") | |
| with tab3: | |
| st.header("Multi-Agent QA (PDF + Weather)") | |
| user_input = st.text_area("Ask multiple questions (e.g. 'What organizations has Sharath worked for and tell me the weather in Mumbai'):") | |
| uploaded_pdf = st.file_uploader("Upload a PDF for PDF Agent (optional)", type=["pdf"], key="multi_pdf") | |
| if st.button("Ask Multi-Agent"): | |
| from nodes.node import split_questions, classify_question | |
| from langchain_core.messages import HumanMessage | |
| import tempfile | |
| messages = [] | |
| # If PDF uploaded, save and use it | |
| pdf_path = None | |
| if uploaded_pdf: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file: | |
| tmp_file.write(uploaded_pdf.read()) | |
| pdf_path = tmp_file.name | |
| # Split and process each question | |
| questions = split_questions(user_input) | |
| for question in questions: | |
| agent_name = classify_question(question) | |
| if agent_name == "pdf_agent": | |
| if pdf_path: | |
| pdf_agent = PDFAgent(pdf_path=pdf_path) | |
| else: | |
| pdf_agent = PDFAgent(pdf_path="Sharath_OnePage.pdf") | |
| result = pdf_agent.agent.invoke({"input": question}) | |
| if isinstance(result, dict): | |
| text_result = result.get("output") or result.get("text") or str(result) | |
| else: | |
| text_result = str(result) | |
| messages.append(("PDF Agent", text_result)) | |
| else: | |
| weather_agent = WeatherAgent() | |
| import re | |
| match = re.search(r"weather in ([\w\s,]+)", question, re.IGNORECASE) | |
| location = match.group(1).strip() if match else question | |
| result = weather_agent.ask(location) | |
| messages.append(("Weather Agent", str(result))) | |
| st.subheader("Results:") | |
| for agent, answer in messages: | |
| st.markdown(f"**{agent}:** {answer}") |