app.py

import os
import requests
import openai
import gradio as gr

# 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 the Proxycurl API
def fetch_linkedin_data(linkedin_url):
    api_key = os.getenv("PROXYCURL_API_KEY")
    headers = {'Authorization': f'Bearer {api_key}'}
    api_endpoint = 'https://nubela.co/proxycurl/api/v2/linkedin'
    
    response = requests.get(api_endpoint,
                            params={'url': linkedin_url},
                            headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        return {"error": f"Error fetching LinkedIn data: {response.text}"}

# Function to fetch company information using Firecrawl API
def fetch_company_info(company_url):
    api_key = os.getenv("FIRECRAWL_API_KEY")
    headers = {
        'Authorization': f'Bearer {api_key}',
        'Content-Type': 'application/json'
    }
    api_endpoint = 'https://api.firecrawl.dev/v1/crawl'
    
    data = {
        "url": company_url,
        "limit": 100,
        "scrapeOptions": {
            "formats": ["markdown", "html"]
        }
    }
    
    response = requests.post(api_endpoint, json=data, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        return {"error": f"Error fetching company information: {response.text}"}

# 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

# Custom Agent class to simulate behavior similar to OpenAI's Swarm framework
class Agent:
    def __init__(self, name, instructions, user_data):
        self.name = name
        self.instructions = instructions
        self.user_data = user_data

    def act(self):
        if self.name == "Data Collection Agent":
            linkedin_info = fetch_linkedin_data(self.user_data['linkedin_url'])
            company_info = fetch_company_info(self.user_data['company_url'])
            return linkedin_info, company_info
        elif self.name == "Email Generation Agent":
            linkedin_info, company_info = self.user_data
            prompt = structure_email(self.user_data[0], linkedin_info, company_info)
            email_content = generate_email_content(OPENAI_API_KEY, prompt)
            return email_content

# Simulated Swarm class to manage agents
class Swarm:
    def __init__(self):
        self.agents = []

    def add_agent(self, agent):
        self.agents.append(agent)

    def run(self):
        for agent in self.agents:
            if agent.name == "Data Collection Agent":
                linkedin_info, company_info = agent.act()
                if "error" in linkedin_info or "error" in company_info:
                    return "Error fetching data. Please check the LinkedIn and company URLs."
                return linkedin_info, company_info

# Function that integrates the agents and manages iterations
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
    }

    # Create a Swarm and add the Data Collection Agent
    email_swarm = Swarm()
    data_collection_agent = Agent("Data Collection Agent", "Collect user inputs and relevant data", user_data)
    email_swarm.add_agent(data_collection_agent)

    # Get data from the Data Collection Agent
    linkedin_info, company_info = email_swarm.run()
    if isinstance(linkedin_info, str):  # If an error message is returned
        return linkedin_info

    # Pass the collected data to the Email Generation Agent
    email_agent = Agent("Email Generation Agent", "Generate the email content", (user_data, linkedin_info, company_info))
    email_content = email_agent.act()

    # Validate and refine the email using a ReAct pattern with a maximum of 3 iterations
    for i in range(3):
        if validate_email(email_content):
            return email_content
        else:
            refined_prompt = f"Refine: {structure_email(user_data, linkedin_info, company_info)}"
            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()