Update app.py

app.py

@@ -19,6 +19,7 @@ class AutonomousEmailAgent:
         self.company_info = None
         self.role_description = None
         self.attempts = 0  # Counter for iterations
+        self.max_attempts = 5  # Set maximum number of iterations
 
     def fetch_linkedin_data(self):
         proxycurl_api_key = os.getenv("PROXYCURL_API_KEY")
@@ -100,7 +101,7 @@ class AutonomousEmailAgent:
                 if choices and "message" in choices[0]:
                     content = choices[0]["message"]["content"]
                     print(f"Content: {content}")
-                    return self.format_email(content)
+                    return self.act_on_llm_instructions(content)
                 else:
                     print("Error: Unrecognized format in LLM response.")
                     return "Error: Unrecognized response format."
@@ -111,6 +112,25 @@ class AutonomousEmailAgent:
             print(f"Error: Unable to connect to Groq Cloud LLM. Status Code: {response.status_code}")
             return "Error: Unable to generate response."
 
+    def act_on_llm_instructions(self, llm_response):
+        if self.attempts >= self.max_attempts:
+            print("Max attempts reached. Proceeding with fallback option.")
+            return self.generate_fallback_email()
+
+        print(f"LLM Instruction: {llm_response}")
+        if "scrape" in llm_response.lower():
+            self.attempts += 1
+            print(f"Scraping attempt {self.attempts}...")
+            return self.autonomous_reasoning()
+        elif "generate_email" in llm_response.lower():
+            return self.format_email(llm_response)
+        elif "fallback" in llm_response.lower():
+            return self.generate_fallback_email()
+        else:
+            print("Error: Unrecognized instruction from LLM. Proceeding with available data.")
+            self.attempts += 1
+            return self.autonomous_reasoning()
+
     def format_email(self, llm_response):
         # Clean and format the email
         lines = [line.strip() for line in llm_response.split("\n") if line.strip()]
@@ -144,6 +164,21 @@ class AutonomousEmailAgent:
 
         return f"{formatted_email}{closing_section}\n{signature}"
 
+    def generate_fallback_email(self):
+        # Generate a basic email if max attempts are reached
+        return f"""Subject: Application for {self.role} at {self.company_name}
+
+Dear Hiring Manager,
+
+I am excited to apply for the {self.role} position at {self.company_name}. My experience and skills align with the role's requirements, and I am confident in my ability to contribute positively.
+
+Best regards,
+{self.user_name}
+Email: {self.email}
+Phone: {self.phone}
+LinkedIn: {self.linkedin}
+"""
+
 # Gradio UI setup remains unchanged
 def gradio_ui():
     name_input = gr.Textbox(label="Your Name", placeholder="Enter your name")
@@ -165,10 +200,8 @@ def gradio_ui():
         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"
+        description="Generates a job application email using dynamic and adaptive reasoning."
     )
-    
     demo.launch()
 
 if __name__ == "__main__":