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
        self.max_attempts = 5  # Set maximum number of iterations

    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()

    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"}]

    def run(self):
        self.fetch_linkedin_data()
        return self.autonomous_reasoning()

    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 ensure it is within {self.word_limit} words.
        """
        
        return self.send_request_to_llm(reasoning_prompt)

    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.act_on_llm_instructions(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."

    def act_on_llm_instructions(self, llm_response):
        if self.attempts >= self.max_attempts:
            print("Max attempts reached. Proceeding with fallback option.")
            return self.generate_fallback_email()

        print(f"LLM Instruction: {llm_response}")
        if "scrape" in llm_response.lower():
            self.attempts += 1
            print(f"Scraping attempt {self.attempts}...")
            return self.autonomous_reasoning()
        elif "generate_email" in llm_response.lower():
            return self.format_email(llm_response)
        elif "fallback" in llm_response.lower():
            return self.generate_fallback_email()
        else:
            print("Error: Unrecognized instruction from LLM. Proceeding with available data.")
            self.attempts += 1
            return self.autonomous_reasoning()

    def format_email(self, llm_response):
        # Clean and format the email
        lines = [line.strip() for line in llm_response.split("\n") if line.strip()]
        formatted_email = "\n\n".join(lines)

        # Truncate the email if it exceeds the word limit
        words = formatted_email.split()
        if len(words) > self.word_limit:
            truncated_email = " ".join(words[:self.word_limit]) + "..."
            formatted_email = truncated_email

        # Add the closing section with a call to action
        closing_section = (
            "\n\nI would appreciate the opportunity to discuss how my background, skills, and passion align with the goals "
            "of 100xEngineers. I am eager to contribute to your mission and support the development of future leaders in technology.\n\n"
            "Thank you for considering my application. I look forward to the possibility of discussing this role further.\n"
        )

        # Prepare the signature
        signature = (
            f"Best regards,\n"
            f"{self.user_name}\n"
            f"Email: {self.email}\n"
            f"Phone: {self.phone}\n"
            f"LinkedIn: {self.linkedin}"
        )

        # Ensure only one "Best regards" section and remove any duplicate signatures
        if "Best regards" in formatted_email:
            formatted_email = formatted_email.split("Best regards")[0].strip()

        return f"{formatted_email}{closing_section}\n{signature}"

    def generate_fallback_email(self):
        # Generate a basic email if max attempts are reached
        return f"""Subject: Application for {self.role} at {self.company_name}

Dear Hiring Manager,

I am excited to apply for the {self.role} position at {self.company_name}. My experience and skills align with the role's requirements, and I am confident in my ability to contribute positively.

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

# 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="Generates a job application email using dynamic and adaptive reasoning."
    )
    demo.launch()

if __name__ == "__main__":
    gradio_ui()