import os
import requests
import streamlit as st
import pandas as pd

# Set API keys
os.environ['OPENAI_API_KEY'] = 'AIzaSyC8tIrUlJcCRnyksbykluUZVvER6ynHQeI'
os.environ['SERPAPI_API_KEY'] = '533b5ecf4f322a3e1fb6c6341fc922267805add7e804f7783949a17175ed2f69'

# Define the directory name for saving CSV files
SAVE_DIR = 'saved_csv_files'

# Ensure the save directory exists
os.makedirs(SAVE_DIR, exist_ok=True)

# Define the functions for your workflow
def industry_research(company_name, industry_name):
    query = f"{company_name} {industry_name} AI trends"
    params = {
        "api_key": os.environ['SERPAPI_API_KEY'],
        "q": query,
        "num": 5
    }
    response = requests.get("https://serpapi.com/search.json", params=params)
    if response.status_code == 200:
        search_results = response.json()
        return search_results.get('organic_results', [])
    else:
        return {"error": response.text}

def summarize_findings(search_results):
    findings = []
    for result in search_results:
        title = result.get('title')
        link = result.get('link')
        description = f"{title} can be explored further at [this link]({link})."
        findings.append(description)
    return findings

def save_results_to_csv(results, company_name):
    df = pd.DataFrame(results)
    csv_file_path = os.path.join(SAVE_DIR, f"{company_name}_research_results.csv")
    df.to_csv(csv_file_path, index=False)
    return csv_file_path  # Return the file path for download

def main():
    st.title("Multi-Agent Workflow for AI Use Cases")

    # Input fields for company and industry
    company_name = st.text_input("Enter the Company Name", "Deloitte")
    industry_name = st.text_input("Enter the Industry Name", "Supply Chain Optimization")

    if st.button("Research"):
        if company_name and industry_name:
            # Conduct industry research
            research_results = industry_research(company_name, industry_name)
            
            # Display results
            if 'error' not in research_results:
                st.subheader(f"Market Research Findings for {company_name} in {industry_name} Industry:")
                findings = summarize_findings(research_results)
                for finding in findings:
                    st.write(finding)

                # Proposed Use Cases Section
                st.subheader("Proposed Use Cases")
                for i, finding in enumerate(findings):
                    st.write(f"{i + 1}. Use Case {i + 1}: {finding}")

                # Resource Links Section
                st.subheader("Resource Links")
                for result in research_results:
                    st.write(f"- [Resource: {result.get('title')}]({result.get('link')})")
                
                # Save results to CSV and provide a download link
                csv_file_path = save_results_to_csv(research_results, company_name)
                with open(csv_file_path, 'rb') as file:
                    st.download_button(label="Download CSV", data=file, file_name=os.path.basename(csv_file_path), mime='text/csv')

            else:
                st.error("Error: " + research_results['error'])
        else:
            st.warning("Please enter both a company name and an industry name.")

if __name__ == "__main__":
    main()