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import os
import requests
import openai
import gradio as gr
from swarm import Swarm, Agent
# Fetch API keys from environment variables
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
PROXYCURL_API_KEY = os.getenv("PROXYCURL_API_KEY")
FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY")
# Function to fetch LinkedIn data using Proxycurl API
def fetch_linkedin_data(linkedin_url):
headers = {'Authorization': f'Bearer {PROXYCURL_API_KEY}'}
response = requests.get(f"https://api.proxycurl.com/v1/linkedin/{linkedin_url}", headers=headers)
if response.status_code == 200:
return response.json()
else:
return {"error": "Unable to fetch LinkedIn data"}
# Function to fetch company information using Firecrawl API
def fetch_company_info(company_url):
headers = {'Authorization': f'Bearer {FIRECRAWL_API_KEY}'}
response = requests.get(f"https://api.firecrawl.com/v1/scrape?url={company_url}", headers=headers)
if response.status_code == 200:
return response.json()
else:
return {"error": "Unable to fetch company information"}
# Function to structure the email using the "Start with Why" model
def structure_email(user_data, linkedin_info, company_info):
why = f"I am passionate about {company_info.get('mission', 'your mission')} because it aligns with my experience as {linkedin_info.get('current_role', 'a professional')}."
how = f"My skills in {user_data['role']} match the requirements and goals of your organization."
what = f"I can bring my experience in {linkedin_info.get('skills', 'relevant skills')} to help achieve {company_info.get('goal', 'your company goals')}."
structured_input = f"{why}\n\n{how}\n\n{what}"
return structured_input
# Function to generate email content using Nvidia Nemotron LLM
def generate_email_content(api_key, prompt):
openai.api_key = api_key
response = openai.Completion.create(
model="nemotron-70b",
prompt=prompt,
max_tokens=500
)
return response.choices[0].text
# Function to validate the generated email for professional tone and completeness
def validate_email(email_content):
return "Why" in email_content and "How" in email_content and "What" in email_content
# Define Agent B - The agent responsible for processing and refining the email
def transfer_to_email_generation():
return email_agent_b
# Define Agent A - The primary agent to initiate data collection and hand over to Agent B
email_agent_a = Agent(
name="Data Collection Agent",
instructions="Collect user inputs and relevant data (LinkedIn and company details).",
functions=[transfer_to_email_generation]
)
# Define Agent B - The agent that structures and generates the email
email_agent_b = Agent(
name="Email Generation Agent",
instructions="Structure the email using the 'Start with Why' model and generate professional content.",
)
# Set up the Swarm client
client = Swarm()
# Main function that interacts with the agents using the Swarm framework
def run_agent(name, email, phone, linkedin_url, company_url, role):
user_data = {
"name": name,
"email": email,
"phone": phone,
"linkedin_url": linkedin_url,
"company_url": company_url,
"role": role
}
# Step 1: Fetch LinkedIn and Company Data
linkedin_info = fetch_linkedin_data(linkedin_url)
company_info = fetch_company_info(company_url)
if "error" in linkedin_info or "error" in company_info:
return "Error fetching data. Please check the LinkedIn and company URLs."
# Step 2: Use the Swarm framework to structure and generate the email
messages = [{"role": "user", "content": "Initiate email generation."}]
response = client.run(agent=email_agent_a, messages=messages)
# Step 3: Agent B structures and refines the email using the provided data
prompt = structure_email(user_data, linkedin_info, company_info)
email_content = generate_email_content(OPENAI_API_KEY, prompt)
# Validate the generated email content using the ReAct pattern (with a max of 3 iterations)
for i in range(3):
if validate_email(email_content):
return email_content
else:
# Refine the prompt based on the feedback or iteration logic
refined_prompt = f"Refine: {prompt}"
email_content = generate_email_content(OPENAI_API_KEY, refined_prompt)
return "Unable to generate a valid email after 3 attempts."
# Set up the Gradio interface
final_interface = gr.Interface(
fn=run_agent,
inputs=[
gr.Textbox(label="Name"),
gr.Textbox(label="Email"),
gr.Textbox(label="Phone Number"),
gr.Textbox(label="LinkedIn Profile URL"),
gr.Textbox(label="Company URL or Name"),
gr.Textbox(label="Role Being Applied For")
],
outputs="text",
title="Email Writing AI Agent",
description="Autonomously generate a professional email tailored to the job application."
)
if __name__ == "__main__":
final_interface.launch()
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