app.py

import streamlit as st
from gpt_researcher import GPTResearcher
import asyncio
import nest_asyncio
import os

# Access secrets
openai_api_key = st.secrets["OPENAI_API_KEY"]
tavily_api_key = st.secrets["TAVILY_API_KEY"]

# Apply the asyncio patch from nest_asyncio if required
nest_asyncio.apply()

# Set the document path environment variable
os.environ["DOC_PATH"] = "./local"  # Path to the folder with documents

# Constants
REPORT_TYPE = "research_report"


# Define the asynchronous function to fetch the report
async def fetch_report(query, report_type):
    """
    Fetch a research report based on the provided query and report type.
    Research is conducted on a local document related to Google Search Algorithm Leak.
    """
    try:
        researcher = GPTResearcher(
            query=query, report_type=report_type, report_source="local"
        )
        await researcher.conduct_research()
        return await researcher.write_report()
    except Exception as e:
        return f"Error during research: {str(e)}"


# Function to run the asynchronous function in a separate thread
def run_async(coroutine):
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    return loop.run_until_complete(coroutine)


# Streamlit interface
st.title("Google Leak Reporting Tool")

# User input for the query using a text area
query = st.text_area(
    "Enter your research query:",
    "Extract all the information about how the ranking for internal links works.",
    height=150,  # Adjustable height
)

# Start the report generation process
if st.button("Generate Report"):
    if not query:
        st.warning("Please enter a query to generate a report.")
    else:
        with st.spinner("Generating report..."):
            # Submit the task to the ThreadPoolExecutor
            future = executor.submit(run_async, fetch_report(query, REPORT_TYPE))
            # Wait for the result
            report = future.result()
            # Display the report or error message
            if report and not report.startswith("Error"):
                st.success("Report generated successfully!")
                st.write(report)  # Display the report in the app
                # Create a download button for the report
                st.download_button(
                    label="Download Report as Text File",
                    data=report,
                    file_name="research_report.txt",
                    mime="text/plain",
                )
            else:
                st.error(report)  # Show the error message if any