Update app.py

app.py

@@ -12,7 +12,7 @@ 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 sanitize data by ensuring it's safe and clean for use
+# Function to sanitize and validate data
 def sanitize_data(data, default_value=""):
     return data.strip() if isinstance(data, str) and data.strip() else default_value
 
@@ -33,10 +33,10 @@ def fetch_linkedin_data(linkedin_url):
             return response.json()
         else:
             logging.error(f"Error fetching LinkedIn data: {response.text}")
-            return {"error": f"Error fetching LinkedIn data: {response.text}"}
+            return None
     except Exception as e:
         logging.error(f"Exception during LinkedIn data fetch: {e}")
-        return {"error": f"Exception during LinkedIn data fetch: {e}"}
+        return None
 
 # Function to fetch company information using Firecrawl API
 def fetch_company_info(company_url):
@@ -63,74 +63,52 @@ def fetch_company_info(company_url):
             return response.json()
         else:
             logging.error(f"Error fetching company information: {response.text}")
-            return {"error": f"Error fetching company information: {response.text}"}
+            return None
     except Exception as e:
         logging.error(f"Exception during company info fetch: {e}")
-        return {"error": f"Exception during company info fetch: {e}"}
+        return None
 
 # Function to structure the email dynamically based on inputs and fetched data
 def structure_email(user_data, linkedin_info, company_info):
-    # Sanitize and extract the required information
     linkedin_role = sanitize_data(linkedin_info.get('current_role', user_data['role']))
-    linkedin_skills = sanitize_data(linkedin_info.get('skills', 'various relevant skills'))
+    linkedin_skills = sanitize_data(linkedin_info.get('skills', 'relevant skills'))
     linkedin_industry = sanitize_data(linkedin_info.get('industry', 'the industry'))
     company_name = sanitize_data(user_data['company_url'] or company_info.get('company_name', 'the company'))
     company_mission = sanitize_data(company_info.get('mission', f"{company_name}'s mission"))
     company_goal = sanitize_data(company_info.get('goal', 'achieving excellence'))
 
-    # Build the email directly with the available and sanitized information
-    email_body = f"Dear Hiring Manager,\n\n"
-    email_body += f"I am writing to express my interest in the {sanitize_data(user_data['role'])} position at {company_name}. "
-    email_body += f"I am particularly drawn to {company_name}'s mission to {company_mission}, which aligns with my passion and expertise in {linkedin_industry}. "
-    email_body += f"As a {linkedin_role}, I have developed skills in {linkedin_skills}, which I believe are highly relevant to the requirements of this role.\n\n"
-    email_body += f"I am confident that my background and expertise in {linkedin_skills} can contribute to achieving {company_goal}. "
-    email_body += f"My experience in similar roles has prepared me to make an immediate and meaningful impact on your team, and I am excited about the opportunity to bring my expertise to {company_name}.\n\n"
-    email_body += f"I would appreciate the opportunity to discuss how my background and skills align with the needs of your organization. "
-    email_body += "Thank you for your time and consideration. I look forward to the possibility of contributing to your team.\n\n"
-    email_body += f"Best regards,\n{sanitize_data(user_data['name'])}"
-    
+    # Construct the email with fully sanitized and available data
+    email_body = (
+        f"Dear Hiring Manager,\n\n"
+        f"I am writing to express my interest in the {sanitize_data(user_data['role'])} position at {company_name}. "
+        f"{company_mission} aligns closely with my professional experience in {linkedin_industry}. "
+        f"As a {linkedin_role}, I have developed expertise in {linkedin_skills}, which are highly relevant to this role.\n\n"
+        f"My background in {linkedin_skills} will contribute significantly to {company_goal}. "
+        f"I am eager to bring my expertise to {company_name} and collaborate with your team.\n\n"
+        f"I would appreciate the opportunity to discuss how my background aligns with the needs of your organization. "
+        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{sanitize_data(user_data['name'])}"
+    )
+
     return email_body
 
-# Function to validate the generated email for completeness and professionalism
-def validate_email(email_content):
+# Function to validate the generated email based on critical components
+def validate_email(email_content, user_data):
     logging.info("Validating email content...")
-    logging.info(f"Email Content for Validation: {email_content}")
 
-    # Check if essential elements exist in the email content
-    return all(keyword in email_content for keyword in ["interest", "skills", "experience", "contribute", "Best regards"])
+    # Validate the presence of essential details in the email
+    required_keywords = [
+        user_data['name'],
+        user_data['role'],
+        "skills",
+        "experience",
+        "contribute",
+        "Best regards"
+    ]
 
-# Function to generate email content using Nvidia Nemotron LLM (non-streaming for simplicity)
-def generate_email_content(api_key, prompt):
-    client = OpenAI(
-        base_url="https://integrate.api.nvidia.com/v1",
-        api_key=api_key
-    )
-    
-    logging.info("Generating email content...")
-    try:
-        response = client.chat.completions.create(
-            model="nvidia/llama-3.1-nemotron-70b-instruct",
-            messages=[
-                {"role": "user", "content": prompt}
-            ],
-            temperature=0.5,
-            top_p=1,
-            max_tokens=1024,
-            stream=False
-        )
-        
-        if hasattr(response, 'choices') and len(response.choices) > 0:
-            email_content = response.choices[0].message.content
-            logging.info("Email content generated successfully.")
-            return email_content
-        else:
-            logging.error("Error: No choices found in the response.")
-            return "Error generating email content: No valid choices."
-    except Exception as e:
-        logging.error(f"Error generating email content: {e}")
-        return "Error generating email content."
+    return all(keyword in email_content for keyword in required_keywords)
 
-# Custom Agent class to simulate behavior similar to OpenAI's Swarm framework
+# Custom Agent class following ReAct pattern
 class Agent:
     def __init__(self, name, instructions, user_data):
         self.name = name
@@ -141,15 +119,17 @@ class Agent:
         if self.name == "Data Collection Agent":
             linkedin_info = fetch_linkedin_data(self.user_data['linkedin_url'])
             company_info = fetch_company_info(self.user_data['company_url'])
-            return linkedin_info, company_info
+            if linkedin_info and company_info:
+                return linkedin_info, company_info
+            else:
+                return None, None
         elif self.name == "Email Generation Agent":
-            user_data = self.user_data['user_data']
             linkedin_info = self.user_data['linkedin_info']
             company_info = self.user_data['company_info']
-            email_content = structure_email(user_data, linkedin_info, company_info)
-            return email_content
+            prompt = structure_email(self.user_data['user_data'], linkedin_info, company_info)
+            return prompt
 
-# Simulated Swarm class to manage agents
+# Simulated Swarm class to manage multiple agents
 class Swarm:
     def __init__(self):
         self.agents = []
@@ -158,14 +138,13 @@ class Swarm:
         self.agents.append(agent)
 
     def run(self):
-        for agent in self.agents:
-            if agent.name == "Data Collection Agent":
-                linkedin_info, company_info = agent.act()
-                if "error" in linkedin_info or "error" in company_info:
-                    return "Error fetching data. Please check the LinkedIn and company URLs."
-                return linkedin_info, company_info
+        # The data collection agent acts first
+        linkedin_info, company_info = self.agents[0].act()
+        if not linkedin_info or not company_info:
+            return "Error: Could not retrieve data for LinkedIn or company information."
+        return linkedin_info, company_info
 
-# Function that integrates the agents and manages iterations
+# Function to run the agent, using Swarm and ReAct
 def run_agent(name, email, phone, linkedin_url, company_url, role):
     user_data = {
         "name": name,
@@ -176,6 +155,7 @@ def run_agent(name, email, phone, linkedin_url, company_url, role):
         "role": role
     }
 
+    # Initialize 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)
@@ -193,12 +173,14 @@ def run_agent(name, email, phone, linkedin_url, company_url, role):
     email_agent = Agent("Email Generation Agent", "Generate the email content", agent_data)
     email_content = email_agent.act()
 
-    for i in range(3):
-        if validate_email(email_content):
+    # Iterative refinement using ReAct pattern
+    max_iterations = 3
+    for i in range(max_iterations):
+        if validate_email(email_content, user_data):
             return email_content
         else:
-            refined_prompt = f"Refine: {structure_email(user_data, linkedin_info, company_info)}"
-            email_content = generate_email_content(OPENAI_API_KEY, refined_prompt)
+            logging.info(f"Iteration {i+1}: Refining email...")
+            email_content = structure_email(user_data, linkedin_info, company_info)
 
     return "Unable to generate a valid email after 3 attempts."