Update app.py

app.py

@@ -22,7 +22,7 @@ def fetch_linkedin_data(linkedin_url):
     response = requests.get(api_endpoint,
                             params={'url': linkedin_url},
                             headers=headers,
-                            timeout=10)  # Adding a timeout for safety
+                            timeout=10)
     if response.status_code == 200:
         logging.info("LinkedIn data fetched successfully.")
         return response.json()
@@ -48,7 +48,7 @@ def fetch_company_info(company_url):
     }
     
     logging.info("Fetching company information...")
-    response = requests.post(api_endpoint, json=data, headers=headers, timeout=15)  # Adding a timeout for safety
+    response = requests.post(api_endpoint, json=data, headers=headers, timeout=15)
     if response.status_code == 200:
         logging.info("Company information fetched successfully.")
         return response.json()
@@ -56,13 +56,45 @@ def fetch_company_info(company_url):
         logging.error(f"Error fetching company information: {response.text}")
         return {"error": f"Error fetching company information: {response.text}"}
 
-# Function to structure the email using the "Start with Why" model
+# Function to structure the email dynamically based on inputs and fetched data
 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
+    # Fetch relevant information from the LinkedIn profile and company information
+    linkedin_role = linkedin_info.get('current_role', 'a professional')
+    linkedin_skills = linkedin_info.get('skills', 'various relevant skills')
+    company_mission = company_info.get('mission', 'your mission')
+    company_goal = company_info.get('goal', 'achieving excellence in the field')
+
+    # Construct the dynamic email content based on the provided and fetched information
+    email_content = (
+        f"Dear Hiring Manager,\n\n"
+        f"I am writing to express my enthusiasm for the {user_data['role']} position at {user_data['company_url']}. "
+        f"The mission of {company_mission} resonates deeply with me, as my professional experience aligns closely with this vision.\n\n"
+        f"Having worked as {linkedin_role}, I have developed skills in {linkedin_skills}. These skills match the needs of your organization, "
+        f"and I am confident in my ability to contribute effectively to {company_goal}.\n\n"
+        f"I am eager to bring my expertise in {linkedin_skills} to your team, focusing on achieving key objectives and supporting your projects. "
+        f"My goal is to make a meaningful impact and collaborate with like-minded professionals committed to excellence.\n\n"
+        f"I would appreciate the opportunity to discuss how my background and skills align with the needs of {user_data['company_url']}. "
+        f"Please find my resume attached for a more detailed overview of my qualifications.\n\n"
+        f"Thank you for your time and consideration. I look forward to the possibility of contributing to your team.\n\n"
+        f"Best regards,\n"
+        f"{user_data['name']}"
+    )
+    return email_content
+
+# Function to validate the generated email for professional tone and completeness
+def validate_email(email_content):
+    logging.info("Validating email content...")
+    logging.info(f"Email Content for Validation: {email_content}")
+
+    # Check if the generated email contains essential elements
+    if ("enthusiasm" in email_content and
+        "skills" in email_content and
+        "contribute" in email_content):
+        logging.info("Email content validation passed.")
+        return True
+    else:
+        logging.info("Email content validation failed.")
+        return False
 
 # Function to generate email content using Nvidia Nemotron LLM (non-streaming for simplicity)
 def generate_email_content(api_key, prompt):
@@ -84,7 +116,6 @@ def generate_email_content(api_key, prompt):
             stream=False  # Disable streaming for simplicity
         )
         
-        # Access the response content correctly based on the Nvidia API structure
         if hasattr(response, 'choices') and len(response.choices) > 0:
             email_content = response.choices[0].message.content
             logging.info("Email content generated successfully.")
@@ -97,21 +128,6 @@ def generate_email_content(api_key, prompt):
         logging.error(f"Error generating email content: {e}")
         return "Error generating email content."
 
-# Function to validate the generated email for professional tone and completeness
-def validate_email(email_content):
-    logging.info("Validating email content...")
-    logging.info(f"Email Content for Validation: {email_content}")
-
-    # Adjust validation to check for structure rather than exact keywords
-    if ("passionate" in email_content and
-        "skills" in email_content and
-        "experience" in email_content):
-        logging.info("Email content validation passed.")
-        return True
-    else:
-        logging.info("Email content validation failed.")
-        return False
-
 # Custom Agent class to simulate behavior similar to OpenAI's Swarm framework
 class Agent:
     def __init__(self, name, instructions, user_data):
@@ -159,28 +175,23 @@ def run_agent(name, email, phone, linkedin_url, company_url, role):
         "role": role
     }
 
-    # Create a Swarm and add the Data Collection Agent
     email_swarm = Swarm()
     data_collection_agent = Agent("Data Collection Agent", "Collect user inputs and relevant data", user_data)
     email_swarm.add_agent(data_collection_agent)
 
-    # Get data from the Data Collection Agent
     linkedin_info, company_info = email_swarm.run()
-    if isinstance(linkedin_info, str):  # If an error message is returned
+    if isinstance(linkedin_info, str):
         return linkedin_info
 
-    # Create a structured dictionary for the Email Generation Agent
     agent_data = {
         "user_data": user_data,
         "linkedin_info": linkedin_info,
         "company_info": company_info
     }
 
-    # Pass the collected data to the Email Generation Agent
     email_agent = Agent("Email Generation Agent", "Generate the email content", agent_data)
     email_content = email_agent.act()
 
-    # Validate and refine the email using a ReAct pattern with a maximum of 3 iterations
     for i in range(3):
         if validate_email(email_content):
             return email_content