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import requests
import gradio as gr
import os
# 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
firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY") # Firecrawl 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: Deciding what data we need...")
if not self.linkedin_url:
print("Warning: LinkedIn URL missing. Proceeding with default bio.")
if not self.company_name:
print("Warning: Company name missing. Proceeding with default company info.")
if not self.role:
print("Warning: Role missing. We will use general logic for the role.")
# Action: Fetch LinkedIn data via Proxycurl (acting based on reasoning)
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 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"]
# Action: Fetch company information via Firecrawl API
def fetch_company_info_with_firecrawl(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."
else:
print(f"Action: Fetching company info for {self.company_name} using Firecrawl.")
headers = {"Authorization": f"Bearer {firecrawl_api_key}"}
firecrawl_url = "https://api.firecrawl.dev/v1/scrape"
data = {
"url": f"https://{self.company_name}.com",
"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."
# Reflection: Check if we have enough data to generate the email
def reflect_on_data(self):
print("Reflection: Do we have enough data?")
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
# Final Action: Generate the email using Groq Cloud LLM based on gathered data
def generate_email(self):
print("Action: Generating the email with the gathered information.")
# Dynamic LLM prompt
prompt = f"""
Write a professional email applying for the {self.role} position at {self.company_name}.
Use the following information:
- 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])}.
Please research the company's public information. If no company-specific information is available, use general knowledge about the company's industry.
Tailor the email dynamically to the role of **{self.role}** at {self.company_name}, aligning the candidate's skills and experiences with the expected responsibilities of the role and the company’s operations.
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() # Reasoning step
self.fetch_linkedin_data() # Fetch LinkedIn data
self.fetch_company_info_with_firecrawl() # Fetch company data using Firecrawl
# Reflect on whether the data is sufficient
if self.reflect_on_data():
return self.generate_email() # Final action: generate email
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)
# 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()
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