Update app.py

app.py

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+import os
+import requests
+import openai
+import gradio as gr
+from openai_swarm import Agent, Swarm
+
+# Fetch API keys from environment variables
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+PROXYCURL_API_KEY = os.getenv("PROXYCURL_API_KEY")
+FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY")
+
+# Function to fetch LinkedIn data using Proxycurl API
+def fetch_linkedin_data(linkedin_url):
+    headers = {'Authorization': f'Bearer {PROXYCURL_API_KEY}'}
+    response = requests.get(f"https://api.proxycurl.com/v1/linkedin/{linkedin_url}", headers=headers)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        return {"error": "Unable to fetch LinkedIn data"}
+
+# Function to fetch company information using Firecrawl API
+def fetch_company_info(company_url):
+    headers = {'Authorization': f'Bearer {FIRECRAWL_API_KEY}'}
+    response = requests.get(f"https://api.firecrawl.com/v1/scrape?url={company_url}", headers=headers)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        return {"error": "Unable to fetch company information"}
+
+# Function to structure the email using the "Start with Why" model
+def structure_email(user_data, linkedin_info, company_info):
+    why = f"I am passionate about {company_info.get('mission', 'your mission')} because it aligns with my experience as {linkedin_info.get('current_role', 'a professional')}."
+    how = f"My skills in {user_data['role']} match the requirements and goals of your organization."
+    what = f"I can bring my experience in {linkedin_info.get('skills', 'relevant skills')} to help achieve {company_info.get('goal', 'your company goals')}."
+    structured_input = f"{why}\n\n{how}\n\n{what}"
+    return structured_input
+
+# Function to generate email content using Nvidia Nemotron LLM
+def generate_email_content(api_key, prompt):
+    openai.api_key = api_key
+    response = openai.Completion.create(
+        model="nemotron-70b",
+        prompt=prompt,
+        max_tokens=500
+    )
+    return response.choices[0].text
+
+# Function to validate the generated email for professional tone and completeness
+def validate_email(email_content):
+    # Basic validation: check if the email contains key sections (can be expanded)
+    return "Why" in email_content and "How" in email_content and "What" in email_content
+
+# Define the ReAct Agent using Swarm framework
+class EmailAgent(Agent):
+    def __init__(self, user_data):
+        super().__init__()
+        self.user_data = user_data
+        self.iterations = 0
+
+    def act(self):
+        linkedin_info = fetch_linkedin_data(self.user_data['linkedin_url'])
+        company_info = fetch_company_info(self.user_data['company_url'])
+        if "error" in linkedin_info or "error" in company_info:
+            return "Error fetching data. Please check the LinkedIn and company URLs."
+        
+        prompt = structure_email(self.user_data, linkedin_info, company_info)
+        email_content = generate_email_content(OPENAI_API_KEY, prompt)
+        return email_content
+
+    def react(self, output):
+        # React based on the generated email's validity
+        if validate_email(output):
+            return output
+        else:
+            # If invalid, refine prompt or retry (up to 3 iterations)
+            self.iterations += 1
+            if self.iterations < 3:
+                refined_prompt = f"Refined: {output}"
+                return generate_email_content(OPENAI_API_KEY, refined_prompt)
+            else:
+                return "Unable to generate a valid email after 3 attempts."
+
+# Define the main function to run the agent within the Swarm
+def run_agent(name, email, phone, linkedin_url, company_url, role):
+    user_data = {
+        "name": name,
+        "email": email,
+        "phone": phone,
+        "linkedin_url": linkedin_url,
+        "company_url": company_url,
+        "role": role
+    }
+    
+    # Initialize the swarm and add the EmailAgent
+    email_swarm = Swarm()
+    agent = EmailAgent(user_data)
+    email_swarm.add_agent(agent)
+    
+    # Run the swarm to get the final email content
+    result = email_swarm.run()
+    return result
+
+# Set up the Gradio interface
+final_interface = gr.Interface(
+    fn=run_agent,
+    inputs=[
+        gr.Textbox(label="Name"),
+        gr.Textbox(label="Email"),
+        gr.Textbox(label="Phone Number"),
+        gr.Textbox(label="LinkedIn Profile URL"),
+        gr.Textbox(label="Company URL or Name"),
+        gr.Textbox(label="Role Being Applied For")
+    ],
+    outputs="text",
+    title="Email Writing AI Agent",
+    description="Autonomously generate a professional email tailored to the job application."
+)
+
+if __name__ == "__main__":
+    final_interface.launch()