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Update app.py
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app.py
CHANGED
@@ -2,6 +2,7 @@ import streamlit as st
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from gpt_researcher import GPTResearcher
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import asyncio
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import nest_asyncio
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# Access secrets
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openai_api_key = st.secrets["OPENAI_API_KEY"]
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@@ -10,36 +11,29 @@ tavily_api_key = st.secrets["TAVILY_API_KEY"]
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# Apply the asyncio patch from nest_asyncio if required
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nest_asyncio.apply()
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# Constants
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REPORT_TYPE = "research_report"
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"""
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Fetch a research report based on the provided query
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"""
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researcher = GPTResearcher(query=query, report_type=report_type, source_urls=sources)
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# Conduct research
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await researcher.conduct_research()
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# Write the report after research has been conducted
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report = await researcher.write_report()
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# Return the completed report
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return report
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# Function to handle asynchronous calls
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def run_async(coroutine):
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loop = asyncio.get_event_loop()
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return loop.run_until_complete(coroutine)
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# Streamlit interface
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st.title("Google Leak Reporting Tool")
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@@ -55,8 +49,8 @@ if st.button("Generate Report"):
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st.warning("Please enter a query to generate a report.")
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else:
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with st.spinner("Generating report..."):
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# Fetch the report asynchronously using
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fetch_report_coroutine = fetch_report(query, REPORT_TYPE
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report = run_async(fetch_report_coroutine)
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st.success("Report generated successfully!")
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st.write(report)
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from gpt_researcher import GPTResearcher
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import asyncio
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import nest_asyncio
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import os
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# Access secrets
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openai_api_key = st.secrets["OPENAI_API_KEY"]
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# Apply the asyncio patch from nest_asyncio if required
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nest_asyncio.apply()
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# Set the document path environment variable
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os.environ['DOC_PATH'] = './' # Path to the folder with documents
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# Constants
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REPORT_TYPE = "research_report"
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DOCUMENT_FILE = 'removed_code.txt' # Name of the document file
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# Function to handle asynchronous calls
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def run_async(coroutine):
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loop = asyncio.get_event_loop()
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return loop.run_until_complete(coroutine)
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# Define the asynchronous function to fetch the report
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async def fetch_report(query, report_type):
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"""
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Fetch a research report based on the provided query and report type.
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Research is conducted on a local document specified by DOCUMENT_FILE.
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"""
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researcher = GPTResearcher(query=query, report_type=report_type, report_source='local')
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await researcher.conduct_research()
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report = await researcher.write_report()
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return report
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# Streamlit interface
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st.title("Google Leak Reporting Tool")
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st.warning("Please enter a query to generate a report.")
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else:
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with st.spinner("Generating report..."):
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# Fetch the report asynchronously using the local document
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fetch_report_coroutine = fetch_report(query, REPORT_TYPE)
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report = run_async(fetch_report_coroutine)
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st.success("Report generated successfully!")
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st.write(report)
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