phase1_agents.py

import os
from phi.agent import Agent
from phi.tools.firecrawl import FirecrawlTools
from phi.tools.duckduckgo import DuckDuckGo
from phi.model.openai import OpenAIChat
from pydantic import BaseModel, Field
from fastapi import UploadFile

# Load environment variables (API keys, etc.)
from dotenv import load_dotenv
load_dotenv()

#####################################################################################
#                                    PHASE 1                                        #
#####################################################################################

##############################
# 1️⃣ Company Search Agent   #
##############################
company_search_agent = Agent(
    name="Company Search Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGo()],
    description="Finds company details based on name using web search.",
    instructions=["Always include sources in search results."],
    show_tool_calls=True,
    markdown=True,
)

def search_company(company_name: str) -> dict:
    """ Searches for detailed company information using web search. """
    query = f"Find details for {company_name}, including its official website, mission, services, and AI-related initiatives. Include sources."
    response = company_search_agent.print_response(query)
    return {"company_name": company_name, "details": response}


##############################
# 2️⃣ Website Scraper Agent   #
##############################
firecrawl_agent = Agent(
    name="Website Scraper Agent",
    tools=[FirecrawlTools(scrape=True, crawl=False)],
    description="Extracts content from company websites.",
    show_tool_calls=True,
    markdown=True,
)

def scrape_website(url: str) -> dict:
    """ Scrapes relevant company information from the given website. """
    response = firecrawl_agent.print_response(f"Extract business details from {url}, including mission, services, and AI-related information.")
    return {"website": url, "scraped_data": response}


##############################
# 3️⃣ Text Processing Agent   #
##############################
class CompanySummary(BaseModel):
    summary: str = Field(..., description="Summarized company details.")

text_processing_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="Summarizes user-written company descriptions.",
    response_model=CompanySummary,
)

def process_company_description(text: str) -> dict:
    """ Summarizes the user-provided company description. """
    response = text_processing_agent.print_response(f"Summarize the following description: {text}. Focus on mission, key services, industry, and AI potential.")
    return {"user_description": text, "summary": response}


##############################
# 4️⃣ Document Processing Agent  #
##############################
def process_uploaded_document(file: UploadFile) -> dict:
    """ Reads and processes an uploaded document, returning extracted content. """
    file_path = f"tmp/{file.filename}"
    with open(file_path, "wb") as buffer:
        buffer.write(file.file.read())

    with open(file_path, "r", encoding="utf-8") as f:
        document_text = f.read()

    return {"document_name": file.filename, "content": document_text}