src/streamlit_app.py

import asyncio # this will help us handle tasks without blocking execution
import streamlit as st 
from typing import Dict, Any, List 
from agents import Agent, Runner, trace
from agents import set_default_openai_key
from firecrawl import FirecrawlApp
from agents.tool import function_tool


## setiing up page configuration

st.set_page_config(
    page_title= "Greg's Personal Deep Research AI Agent",

    page_icon= "📚",

    layout= "wide"
)

## initialize session state for API key 

if "OPENAI_API_KEY" not in st.session_state:
    st.session_state.openai_api_key= ""

if "firecrawl_api_key" not in st.session_state:
    st.session_state.firecrawl_api_key= ""


## sidebar for api key 

with st.sidebar:
    st.title("API Configuration")
    openai_api_key= st.text_input(
        "OPENAI API Key", 
        value= st.session_state.openai_api_key,
        type= "password"
    )

    firecrawl_api_key= st.text_input(
        "FIRECRAWL API KEY", 
        value= st.session_state.firecrawl_api_key,
        type= "password"
    )


    if openai_api_key:
        st.session_state.openai_api_key= openai_api_key
        set_default_openai_key(openai_api_key)

    if firecrawl_api_key:
        st.session_state.firecrawl_api_key= firecrawl_api_key



## Main application and Input Field 

st.title("🔍 Greg's Personal Deep Research AI Agent")
st.markdown("This Agent from OpenAI Agent SDK Performs Deep Research On Any Topic")

#take the user input
research_topic= st.text_input("Enter research topic: ", placeholder= "e.g: Research on the top 10 jobs in 2025 and their salary expectations")

## working with the deep research tool 

#This function tools help us to register the function to the agent 
@function_tool
async def deep_research(query: str, max_depth: int, time_limit: int, max_urls: int):
    """

    Performs comprehensive web research using Firecrawl's deep research endpoint. 
    
    """
    try:
        firecrawl_app= FirecrawlApp(api_key= st.session_state.firecrawl_api_key)
        params= {
            "max_depth": max_depth,
            "time_limit": time_limit,
            "max_urls": max_urls
        }

        def on_activity(activity):
            st.write(f"[{activity['type']}] {activity['message']}") ##helps us track realtime updates 

        ## Run the deep research with firecrawl

        with st.spinner("Performing Deep Research..."):
            resp= firecrawl_app.deep_research(
                query= query,
                on_activity= on_activity,
                **params
            )

        return {
            "success": True,
            "final_analysis": resp["data"]["finalAnalysis"],
            "sources_count": len(resp["data"]["sources"]),
            "sources": resp["data"]["sources"]
        
        }
    except Exception as e:
        st.error(f"Deep Research Error: {str(e)}")
        return{
            "error":str(e),
            "success": False
            
        }


## defining agents for specific task

research_agent= Agent(
    
    name= "research_agent",
    instructions= """ you are a research assistant that can perform deep web research on any topic. When given a research topic, or question:

    1. Use the deep_research tool to gather comprehensive information
        - Always use these parameters:
            - max_depth: 3 (for moderate depth)
            - time_limit: 180 (3 minutes)
            - max_urls:  10 (sufficient resources)
    2. The tool will search the web, analyze multiple sources and provide a synthesis
    3. Review the research result and organize them into a well-structured report 
    4. Include proper citations for all sources
    5.highlight key findings and insights 
    """,

    tools= [deep_research]
)

elaboration_agent= Agent(

    name= "elaboration_agent",

    instructions= """ You are an expert content enhancer specializing in research elaboration.

    When given a research report:
    1. Analyze the structure and content of the report 
    2. Enhance the report by: 
        - Adding more detailed explanation of complex concepts 
        - Include relevant examples, case studies, and real world application. 
        - Expanding on key points with additional context and nuance
        - Adding visual elements descriptions (charts, diagrams, infographics)
        - Incorporating the latest trends and future predictions
        - Suggesting practical implications for different stakeholders

    3.Maintain academic rigor and factually accuracy 
    4. Preserve the original structure as well  while making it more comprehensive 
    5. Ensure all additions are relevant and valuable to topic. 
    """
)


## creating another asynchronous function

async def run_research_process(topic: str):
    """Run the complete research process. """

    ## Step 1- Initial Research 
    with st.spinner("Conducting initial research..."):
        research_results = await Runner.run(research_agent, topic)
        initial_report = research_results.final_output

    ##displaying the initial report 

    with st.expander("View Initial Research Report: "):
        st.markdown(initial_report)

    ## Step 2- Enchance the report

    with st.spinner("Enhancing the report with additional information..."):
        elaboration_input= f"""

        RESEARCH_TOPIC= {topic}

        INITIAL RESEARCH REPORT: {initial_report}

        Please enchance this research report with additional information, examples,case studies, deeper insights while maintaining its academic rigor and factual accuracy
        
        """
        elaboration_result= await Runner.run(elaboration_agent, elaboration_input)
        enhanced_report= elaboration_result.final_output
        
    return enhanced_report 

## Main Research Process

if st.button("Start Research", disabled= not (openai_api_key and firecrawl_api_key and research_topic)):
    if not openai_api_key or not firecrawl_api_key:
        st.warning("Please enter both API keys to start the research!")
    elif not research_topic:
        st.warning("No topic provided! Please enter a research topic")
    else:
        try:
            ## create a placeholder for final report 
            report_placeholder= st.empty()

            # Run the research process
            enhanced_report = asyncio.run(run_research_process(research_topic))

            # Display the results 
            report_placeholder.markdown(f"## {research_topic} Report")
            report_placeholder.markdown(enhanced_report)
            ## Add a download button
            st.download_button(
                "Download Report", 
                enhanced_report,
                file_name= f"{research_topic.replace(' ','_')}_report.md",
                mime= "text/markdown"
            )
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")

## Footer 

st.markdown("----------")
st.markdown("Powered by OpenAI SDK and Firecrawl")