Update app.py

app.py

@@ -1,19 +1,34 @@
 import streamlit as st
 from gpt_researcher import GPTResearcher
+import asyncio
+import nest_asyncio
 import os
+from contextlib import contextmanager
+from io import StringIO
+import sys
 
 # 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 function to fetch the report
-def fetch_report(query, report_type):
+
+# Function to handle asynchronous calls
+def run_async(coroutine):
+    loop = asyncio.get_event_loop()
+    return loop.run_until_complete(coroutine)
+
+
+# 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.
@@ -22,15 +37,11 @@ def fetch_report(query, report_type):
         researcher = GPTResearcher(
             query=query, report_type=report_type, report_source="local"
         )
-        researcher.conduct_research()
-        return researcher.write_report()
+        await researcher.conduct_research()
+        return await researcher.write_report()
     except Exception as e:
         return f"Error during research: {str(e)}"
 
-# Cache the report generation function to avoid redundant computations
-@st.cache(suppress_st_warning=True, show_spinner=False)
-def cached_fetch_report(query, report_type):
-    return fetch_report(query, report_type)
 
 # Streamlit interface
 st.title("Google Leak Reporting Tool")
@@ -48,7 +59,7 @@ if st.button("Generate Report"):
         st.warning("Please enter a query to generate a report.")
     else:
         with st.spinner("Generating report..."):
-            report = cached_fetch_report(query, REPORT_TYPE)
+            report = run_async(fetch_report(query, REPORT_TYPE))
             # Display the report or error message
             if report and not report.startswith("Error"):
                 st.success("Report generated successfully!")