app.py

import gradio as gr
import requests
import os
from bs4 import BeautifulSoup  # Add BeautifulSoup for scraping

# Load API keys securely from environment variables
proxycurl_api_key = os.getenv("PROXYCURL_API_KEY")  # Proxycurl API key
groq_api_key = os.getenv("GROQ_CLOUD_API_KEY")  # Groq Cloud API key

class EmailAgent:
    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

    # Reason: Decide what information is needed
    def reason_about_data(self):
        print("Reasoning: I need LinkedIn data, company info, and role description.")
        if not self.linkedin_url:
            print("Warning: LinkedIn URL missing. Will proceed with default bio.")
        if not self.company_name:
            print("Warning: Company name missing. Will proceed with default company info.")
    
    # Action: Fetch LinkedIn data via Proxycurl
    def fetch_linkedin_data(self):
        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 from 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"]
    
    # Action: Fetch company information via Proxycurl
    def fetch_company_info(self):
        if not self.company_name:
            print("Action: No company name provided, using default company info.")
            self.company_info = "A leading company in its field, offering innovative solutions."
        else:
            print(f"Action: Fetching company info for {self.company_name}.")
            headers = {
                "Authorization": f"Bearer {proxycurl_api_key}",
            }
            url = f"https://nubela.co/proxycurl/api/v2/linkedin/company?company_name={self.company_name}"
            response = requests.get(url, headers=headers)
            if response.status_code == 200:
                data = response.json()
                self.company_info = data.get("description", "No detailed company info available.")
            else:
                print(f"Error: Unable to fetch company info for {self.company_name}. Using default info.")
                self.company_info = "A leading company in its field, offering innovative solutions."

    # Action: Scrape the company's website for role-specific information
    def scrape_role_from_website(self):
        print(f"Action: Scraping role description from the company's website for {self.role}.")
        if not self.company_name:
            print("Error: No company name or URL provided for scraping.")
            return False
        
        # Attempt to scrape the company's website
        try:
            response = requests.get(f"https://{self.company_name}.com/careers")
            if response.status_code == 200:
                soup = BeautifulSoup(response.text, 'html.parser')
                # Look for any sections that might contain role descriptions
                role_descriptions = soup.find_all(string=lambda text: self.role.lower() in text.lower())
                if role_descriptions:
                    # If we find relevant role descriptions, use the first match
                    self.role_description = role_descriptions[0]
                    print(f"Found role description on company's website: {self.role_description}")
                    return True
                else:
                    print(f"No specific role description found on the website for {self.role}.")
                    return False
            else:
                print(f"Error: Unable to reach company's website at {self.company_name}.com.")
                return False
        except Exception as e:
            print(f"Error during scraping: {e}")
            return False

    # Action: Use default logic to infer role description if scraping fails
    def use_default_role_description(self):
        print(f"Action: Using default logic for the role of {self.role}.")
        self.role_description = f"The role of {self.role} at {self.company_name} involves mentoring AI and technology students to develop their skills and progress their careers."

    # Reflection: Check if the data is sufficient to generate an email
    def reflect_on_data(self):
        print("Reflection: Do I have enough data to generate the email?")
        # Allow the email to be generated with default values if data is missing
        if not self.bio or not self.skills or not self.company_info:
            print("Warning: Some critical information is missing. Proceeding with default values.")
        return True
    
    # Action: Generate the email using Groq Cloud LLM
    def generate_email(self):
        print("Action: Generating the email with the gathered information.")
        prompt = f"""
        Write a professional email applying for the {self.role} position at {self.company_name}.
        The candidate’s bio is: {self.bio}.
        
        Focus on relevant skills and experiences from LinkedIn, such as {', '.join(self.skills)}, 
        that directly align with the role of {self.role}. 
        Highlight only the skills and experiences that relate to mentoring, AI, technology, and leadership.
        
        The company info is: {self.company_info}.
        The role involves: {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.
        """
        
        url = "https://api.groq.com/openai/v1/chat/completions"
        headers = {
            "Authorization": f"Bearer {groq_api_key}",
            "Content-Type": "application/json",
        }
        
        data = {
            "messages": [{"role": "user", "content": prompt}],
            "model": "llama3-8b-8192"
        }
        
        response = requests.post(url, headers=headers, json=data)
        if response.status_code == 200:
            return response.json()["choices"][0]["message"]["content"].strip()
        else:
            print(f"Error: {response.status_code}, {response.text}")
            return "Error generating email. Please check your API key or try again later."

    # Main loop following ReAct pattern
    def run(self):
        self.reason_about_data()  # Reason
        self.fetch_linkedin_data()  # Action
        self.fetch_company_info()  # Action
        # Try to scrape the company's website for role-specific information
        if not self.scrape_role_from_website():  
            self.use_default_role_description()  # Use default logic if scraping fails
        if self.reflect_on_data():  # Reflection
            return self.generate_email()  # Final Action
        else:
            return "Error: Not enough data to generate the email."

# 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)  # New slider for word limit
    
    # 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 = EmailAgent(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()