app.py

import gradio as gr
import requests
import os
import json

class AutonomousEmailAgent:
    def __init__(self, linkedin_url, company_name, role, word_limit, user_name, email, phone, linkedin):
        self.linkedin_url = linkedin_url
        self.company_name = company_name
        self.role = role
        self.word_limit = word_limit
        self.user_name = user_name
        self.email = email
        self.phone = phone
        self.linkedin = linkedin
        self.bio = None
        self.skills = []
        self.experiences = []
        self.company_info = None
        self.role_description = None
        self.attempts = 0  # Counter for iterations

    # Fetch LinkedIn data via Proxycurl
    def fetch_linkedin_data(self):
        proxycurl_api_key = os.getenv("PROXYCURL_API_KEY")
        if not self.linkedin_url:
            print("Action: No LinkedIn URL provided, using default bio.")
            self.bio = "A professional with diverse experience."
            self.skills = ["Adaptable", "Hardworking"]
            self.experiences = ["Worked across various industries"]
        else:
            print("Action: Fetching LinkedIn data via Proxycurl.")
            headers = {"Authorization": f"Bearer {proxycurl_api_key}"}
            url = f"https://nubela.co/proxycurl/api/v2/linkedin?url={self.linkedin_url}"
            response = requests.get(url, headers=headers)
            if response.status_code == 200:
                data = response.json()
                self.bio = data.get("summary", "No bio available")
                self.skills = data.get("skills", [])
                self.experiences = data.get("experiences", [])
                print("LinkedIn data fetched successfully.")
            else:
                print("Error: Unable to fetch LinkedIn profile. Status Code:", response.status_code)
                self.use_default_profile()

    # Set default profile information if LinkedIn scraping fails
    def use_default_profile(self):
        print("Using default profile values.")
        self.bio = "A professional with a versatile background and extensive experience."
        self.skills = ["Leadership", "Communication", "Problem-solving"]
        self.experiences = [{"title": "Project Manager"}, {"title": "Team Leader"}]

    # Main loop following ReAct pattern
    def run(self):
        self.fetch_linkedin_data()
        return self.autonomous_reasoning()

    # Reason and Act via LLM: Let the LLM control reasoning and actions dynamically
    def autonomous_reasoning(self):
        print("Autonomous Reasoning: Letting the LLM fully reason and act on available data...")
        
        reasoning_prompt = f"""
        You are an AI agent tasked with generating a job application email using Simon Sinek's Start with Why model. 
        The email must begin with why the candidate is passionate about the role, then explain how their skills and 
        experience align with the company and role, and finally describe specific achievements that demonstrate their 
        capabilities.

        Here’s the current data:
        - LinkedIn profile: {self.linkedin_url}
        - Company Name: {self.company_name}
        - Role: {self.role}
        - Candidate's Bio: {self.bio}
        - Candidate's Skills: {', '.join(self.skills)}
        - Candidate's Experiences: {', '.join([exp['title'] for exp in self.experiences])}

        Generate the email using this structure and make it compelling and tailored to the company and role.
        """
        
        return self.send_request_to_llm(reasoning_prompt)

    # Send request to Groq Cloud LLM with enhanced debugging and error handling
    def send_request_to_llm(self, prompt):
        print("Sending request to Groq Cloud LLM...")
        api_key = os.getenv("GROQ_API_KEY")
        if not api_key:
            print("Error: API key not found. Please set the GROQ_API_KEY environment variable.")
            return "Error: API key not found."
        
        headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        data = {
            "model": "llama-3.1-70b-versatile",
            "messages": [{"role": "user", "content": prompt}]
        }
        response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=data)
        
        print(f"Status Code: {response.status_code}")
        if response.status_code == 200:
            try:
                result = response.json()
                print(f"LLM Response: {json.dumps(result, indent=2)}")
                choices = result.get("choices", [])
                if choices and "message" in choices[0]:
                    content = choices[0]["message"]["content"]
                    print(f"Content: {content}")
                    return self.generate_email(content)
                else:
                    print("Error: Unrecognized format in LLM response.")
                    return "Error: Unrecognized response format."
            except json.JSONDecodeError:
                print("Error: Response from Groq Cloud LLM is not valid JSON.")
                return "Error: Response is not in JSON format."
        else:
            print(f"Error: Unable to connect to Groq Cloud LLM. Status Code: {response.status_code}")
            return "Error: Unable to generate response."

    # Generate email based on the structured response
    def generate_email(self, llm_response):
        print("Generating email based on the structured response...")
        email_content = f"""
        {llm_response}

        Best regards,
        {self.user_name}
        Email: {self.email}
        Phone: {self.phone}
        LinkedIn: {self.linkedin}
        """
        return email_content

# Gradio UI setup remains unchanged
def gradio_ui():
    name_input = gr.Textbox(label="Your Name", placeholder="Enter your name")
    company_input = gr.Textbox(label="Company Name or URL", placeholder="Enter the company name or website URL")
    role_input = gr.Textbox(label="Role Applying For", placeholder="Enter the role you are applying for")
    email_input = gr.Textbox(label="Your Email Address", placeholder="Enter your email address")
    phone_input = gr.Textbox(label="Your Phone Number", placeholder="Enter your phone number")
    linkedin_input = gr.Textbox(label="Your LinkedIn URL", placeholder="Enter your LinkedIn profile URL")
    word_limit_slider = gr.Slider(minimum=50, maximum=300, step=10, label="Email Word Limit", value=150)
    
    email_output = gr.Textbox(label="Generated Email", placeholder="Your generated email will appear here", lines=10)

    def create_email(name, company_name, role, email, phone, linkedin_url, word_limit):
        agent = AutonomousEmailAgent(linkedin_url, company_name, role, word_limit, name, email, phone, linkedin_url)
        return agent.run()

    demo = gr.Interface(
        fn=create_email,
        inputs=[name_input, company_input, role_input, email_input, phone_input, linkedin_input, word_limit_slider],
        outputs=[email_output],
        title="Email Writing AI Agent with ReAct",
        description="Generate a professional email for a job application using LinkedIn data, company info, and role description.",
        allow_flagging="never"
    )
    
    demo.launch()

if __name__ == "__main__":
    gradio_ui()