app.py

import gradio as gr
import requests
import os
from openai import OpenAI

# Initialize the NVIDIA LLM client
client = OpenAI(
    base_url="https://integrate.api.nvidia.com/v1",
    api_key=os.getenv("NVIDIA_API_KEY")  # Use your NVIDIA API key
)

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.company_url = None  # Add company URL for scraping

    # 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...")
        
        # Modify LLM reasoning prompt to ask for clear, structured instructions
        reasoning_prompt = f"""
        You are an autonomous agent responsible for generating a job application email.
        
        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])}
        - Company Information: {self.company_info}
        - Role Description: {self.role_description}
        
        Based on this data, decide if it is sufficient to generate the email. If some information is missing or insufficient, respond with:
        1. "scrape" to fetch more data from the company website.
        2. "generate_email" to proceed with the email generation.
        3. "fallback" to use default values.

        After generating the email, reflect on whether the content aligns with the role and company and whether any improvements are needed. Respond clearly with one of the above options.
        """
        
        # Send the reasoning prompt to the LLM
        completion = client.chat.completions.create(
            model="nvidia/llama-3.1-nemotron-70b-instruct",
            messages=[{"role": "user", "content": reasoning_prompt}],
            temperature=0.5,
            top_p=1,
            max_tokens=1024,
            stream=True
        )

        reasoning_output = ""
        for chunk in completion:
            if chunk.choices[0].delta.content is not None:
                print(chunk.choices[0].delta.content, end="")
                reasoning_output += chunk.choices[0].delta.content

        return self.act_on_llm_instructions(reasoning_output)

    # Function to act on the LLM's structured instructions
    def act_on_llm_instructions(self, reasoning_output):
        instruction = reasoning_output.lower().strip()

        if "scrape" in instruction:
            self.fetch_company_url()
            if self.company_url:
                self.fetch_company_info_with_firecrawl(self.company_url)
            return self.autonomous_reasoning()

        elif "generate_email" in instruction:
            return self.generate_email()

        elif "fallback" in instruction:
            print("Action: Using fallback values for missing data.")
            if not self.company_info:
                self.company_info = "A leading company in its field."
            if not self.role_description:
                self.role_description = f"The role of {self.role} involves leadership and team management."
            return self.generate_email()

        else:
            print("Error: Unrecognized instruction from LLM. Proceeding with available data.")
            return self.generate_email()

    # Fetch company URL using SERP API
    def fetch_company_url(self):
        serp_api_key = os.getenv("SERP_API_KEY")  # Fetch the SERP API key from the environment
        print(f"Fetching company URL for {self.company_name} using SERP API...")
        
        serp_url = f"https://serpapi.com/search.json?q={self.company_name}&api_key={serp_api_key}&num=1"
        response = requests.get(serp_url)
        
        if response.status_code == 200:
            serp_data = response.json()
            if 'organic_results' in serp_data and len(serp_data['organic_results']) > 0:
                self.company_url = serp_data['organic_results'][0]['link']
                print(f"Found company URL: {self.company_url}")
            else:
                print("No URL found for the company via SERP API.")
                self.company_url = None
        else:
            print(f"Error fetching company URL: {response.status_code}")
            self.company_url = None

    # Fetch LinkedIn data via Proxycurl
    def fetch_linkedin_data(self):
        proxycurl_api_key = os.getenv("PROXYCURL_API_KEY")  # Fetch API key from environment
        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", [])
            else:
                print("Error: Unable to fetch LinkedIn profile. Using default bio.")
                self.bio = "A professional with diverse experience."
                self.skills = ["Adaptable", "Hardworking"]
                self.experiences = ["Worked across various industries"]

    # Fetch company information via Firecrawl API using company URL
    def fetch_company_info_with_firecrawl(self, company_url):
        firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY")  # Fetch the Firecrawl API key from the environment
        print(f"Fetching company info for {company_url} using Firecrawl.")
        
        headers = {"Authorization": f"Bearer {firecrawl_api_key}"}
        firecrawl_url = "https://api.firecrawl.dev/v1/scrape"
        data = {
            "url": company_url,
            "patterns": ["description", "about", "careers", "company overview"]
        }
        
        response = requests.post(firecrawl_url, json=data, headers=headers)
        if response.status_code == 200:
            firecrawl_data = response.json()
            self.company_info = firecrawl_data.get("description", "No detailed company info available.")
            print(f"Company info fetched: {self.company_info}")
        else:
            print(f"Error: Unable to fetch company info via Firecrawl. Using default info.")
            self.company_info = "A leading company in its field."

    # Final Action: Generate the email using NVIDIA LLM with "Start with Why" framework
    def generate_email(self):
        print("Action: Generating the email using NVIDIA LLM with the gathered information.")

        linkedin_text = f"Please find my LinkedIn profile at {self.linkedin}" if self.linkedin else ""

        prompt = f"""
        Write a professional job application email applying for the {self.role} position at {self.company_name}.

        The email should follow the "Start with Why" approach:
        1. **Why**: Begin with the candidate’s **purpose** or **belief**—why they are passionate about this role and the company. What motivates them to apply for this role? Connect their personal mission to the company's values, mission, or goals.
        2. **How**: Explain how the candidate’s skills, experience, and approach align with both their "why" and the company’s mission. This should show how they are uniquely qualified to contribute to the company’s success.
        3. **What**: Provide concrete examples of the candidate’s past achievements that support their qualifications for this role. These examples should demonstrate the candidate’s ability to succeed based on their skills and experience.
        4. **Call to Action**: End with a polite request for a meeting or further discussion to explore how the candidate can contribute to the company's success.

        Use the following information to craft the email:
        - The candidate’s LinkedIn bio: {self.bio}.
        - The candidate’s most relevant skills: {', '.join(self.skills)}.
        - The candidate’s professional experience: {', '.join([exp['title'] for exp in self.experiences])}.
        - Company information: {self.company_info}.
        - Role description: {self.role_description}.

        End the email with this signature:
        Best regards,
        {self.user_name}
        Email: {self.email}
        Phone: {self.phone}
        LinkedIn: {self.linkedin}

        The email should not exceed {self.word_limit} words.
        """

        completion = client.chat.completions.create(
            model="nvidia/llama-3.1-nemotron-70b-instruct",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.5,
            top_p=1,
            max_tokens=1024,
            stream=True
        )

        generated_email = ""
        for chunk in completion:
            if chunk.choices[0].delta.content is not None:
                print(chunk.choices[0].delta.content, end="")
                generated_email += chunk.choices[0].delta.content

        return generated_email

    # Main loop following ReAct pattern
    def run(self):
        self.fetch_linkedin_data()  # Fetch LinkedIn data
        return self.autonomous_reasoning()  # Let the LLM autonomously decide and act

# Define the Gradio interface and the main app logic
def gradio_ui():
    # Input fields
    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)
    
    # Output field
    email_output = gr.Textbox(label="Generated Email", placeholder="Your generated email will appear here", lines=10)

    # Function to create and run the email agent
    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()

    # Gradio interface
    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"
    )
    
    # Launch the Gradio app
    demo.launch()

# Start the Gradio app when running the script
if __name__ == "__main__":
    gradio_ui()