Update app.py

app.py

@@ -27,12 +27,11 @@ class AutonomousEmailAgent:
     def autonomous_reasoning(self):
         print("Autonomous Reasoning: Letting the LLM fully reason and act on available data...")
         
-        # LLM reasoning prompt that evaluates the current data and issues actions autonomously
+        # Modify LLM reasoning prompt to ask for clear, structured instructions
         reasoning_prompt = f"""
         You are an autonomous agent responsible for generating a job application email.
         
-        Here's the current data:
-        
+        Here’s the current data:
         - LinkedIn profile: {self.linkedin_url}
         - Company Name: {self.company_name}
         - Role: {self.role}
@@ -42,13 +41,14 @@ class AutonomousEmailAgent:
         - Company Information: {self.company_info}
         - Role Description: {self.role_description}
         
-        Based on this data, decide if it is sufficient to generate the email. If some information is missing or insufficient:
-        - Decide whether to scrape the company's website for more information or use a fallback.
-        - If the scraping fails, decide what next steps to take.
+        Based on this data, decide if it is sufficient to generate the email. If some information is missing or insufficient, respond with:
+        1. "scrape" to fetch more data from the company website.
+        2. "generate_email" to proceed with the email generation.
+        3. "fallback" to use default values.
 
-        Once the information is complete, proceed to generate the email. After generating the email, reflect on whether the content aligns with the role and company and whether any improvements are needed.
+        After generating the email, reflect on whether the content aligns with the role and company and whether any improvements are needed. Respond clearly with one of the above options.
         """
-
+        
         # Send the reasoning prompt to the LLM
         url = "https://api.groq.com/openai/v1/chat/completions"
         headers = {
@@ -72,21 +72,34 @@ class AutonomousEmailAgent:
             print(f"Error: {response.status_code}, {response.text}")
             return "Error: Unable to complete reasoning."
 
-    # Function to act on the LLM's instructions
+    # Function to act on the LLM's structured instructions
     def act_on_llm_instructions(self, reasoning_output):
-        # Parse the LLM's instructions (assume it's structured with action steps)
-        if "scrape the company's website" in reasoning_output:
+        # Convert the output to lowercase and trim whitespace for easier parsing
+        instruction = reasoning_output.lower().strip()
+
+        if "scrape" in instruction:
             # Action: Scrape the company's website for more information
             self.fetch_company_info_with_firecrawl()
             # Reflect again by invoking the LLM to reassess
             return self.autonomous_reasoning()
 
-        elif "generate the email" in reasoning_output:
+        elif "generate_email" in instruction:
             # Action: Proceed to generate the email
             return self.generate_email()
 
+        elif "fallback" in instruction:
+            # Action: Use fallback logic or default values
+            print("Action: Using fallback values for missing data.")
+            if not self.company_info:
+                self.company_info = "A leading company in its field."
+            if not self.role_description:
+                self.role_description = f"The role of {self.role} involves leadership and team management."
+            return self.generate_email()
+
         else:
-            return "Error: Unrecognized instruction from LLM."
+            # If the LLM returns an unrecognized instruction, fall back to using the current available data
+            print("Error: Unrecognized instruction from LLM. Proceeding with available data.")
+            return self.generate_email()
 
     # Action: Fetch LinkedIn data via Proxycurl
     def fetch_linkedin_data(self):