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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() | |