import os
import requests
import openai
import gradio as gr
from openai_swarm import Agent, Swarm

# 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):
    # Basic validation: check if the email contains key sections (can be expanded)
    return "Why" in email_content and "How" in email_content and "What" in email_content

# Define the ReAct Agent using Swarm framework
class EmailAgent(Agent):
    def __init__(self, user_data):
        super().__init__()
        self.user_data = user_data
        self.iterations = 0

    def act(self):
        linkedin_info = fetch_linkedin_data(self.user_data['linkedin_url'])
        company_info = fetch_company_info(self.user_data['company_url'])
        if "error" in linkedin_info or "error" in company_info:
            return "Error fetching data. Please check the LinkedIn and company URLs."
        
        prompt = structure_email(self.user_data, linkedin_info, company_info)
        email_content = generate_email_content(OPENAI_API_KEY, prompt)
        return email_content

    def react(self, output):
        # React based on the generated email's validity
        if validate_email(output):
            return output
        else:
            # If invalid, refine prompt or retry (up to 3 iterations)
            self.iterations += 1
            if self.iterations < 3:
                refined_prompt = f"Refined: {output}"
                return generate_email_content(OPENAI_API_KEY, refined_prompt)
            else:
                return "Unable to generate a valid email after 3 attempts."

# Define the main function to run the agent within the Swarm
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
    }
    
    # Initialize the swarm and add the EmailAgent
    email_swarm = Swarm()
    agent = EmailAgent(user_data)
    email_swarm.add_agent(agent)
    
    # Run the swarm to get the final email content
    result = email_swarm.run()
    return result

# 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()