Update app.py

app.py

@@ -204,70 +204,7 @@ A culturally authentic and conversational response to the question: '{question}'
     
     return responses
 
-
-# MAIN
-if __name__ == "__main__":
-#    Config.load_environment(".", "genz.dev1")
-#    Config.print_environment()
-
-#    processor_llm = get_processor_llm_instance()
-#    respondent_agent_llm = get_respondent_agent_llm_instance()
-
-    logging.info("Loading environment...")
-    Config.load_environment(".", "genz.dev1")
-    
-    logging.info("Environment loaded. Printing environment:")
-    Config.print_environment()
-
-    logging.info("Initializing processor_llm...")
-    processor_llm = get_processor_llm_instance()
-    logging.info("processor_llm initialized.")
-
-    logging.info("Initializing respondent_agent_llm...")
-    respondent_agent_llm = get_respondent_agent_llm_instance()
-    logging.info("respondent_agent_llm initialised.")
-    
-    # Load all user profiles from the Excel file
-    data_dictionary                = DataDictionary.generate_dictionary(Config.data_dictionary_file)
-    logging.info(f"Generated data dictionary: {data_dictionary}")
-    personality_assessment         = PVAssessment.generate_personality_assessment(Config.personality_question_file)
-    logging.info(f"Generated personality_assessment: {data_dictionary}")
-    respondent_agent_user_profiles = UserProfile.read_user_profiles_from_excel(Config.respondent_details_file, data_dictionary, personality_assessment)
-
-    # Create respondent agents for all profiles
-    respondent_agents_dict = {
-        profile.get_field("Demographics", "Name"): RespondentAgent.create(
-            profile, f"{Config.config_dir}/fastfacts/{profile.ID}_fast_facts.xlsx", respondent_agent_llm
-        )
-        for profile in respondent_agent_user_profiles[:5]
-    }
-            
-    def chatbot_interface(message, history=None):
-        """
-        Handles chatbot interaction. Can be used both in Gradio and from MAIN.
-        """
-        if history is None:
-            history = []  # Ensure history is initialized
-
-        responses = ask_interview_question(respondent_agents_dict, message, processor_llm)
-        logging.info(f"Interview response is {responses}")
-
-        # Ensure responses is always a list
-        if not isinstance(responses, list):
-            responses = [responses]  # Wrap single response in a list
-        
-        # Format each response properly
-        formatted_responses = []
-        for r in responses:
-            formatted_responses.append({"role": "assistant", "content": str(r)})
-        
-        # Append user message and formatted responses to history
-        history.append({"role": "user", "content": message})
-        history.extend(formatted_responses)  # Add each response separately
-
-        logging.info(f"Return history: {history}")
-        return history, ""
-
+def build_interface():    
     custom_css = """
     body {
         background-color: A9A9A9; 
@@ -400,5 +337,64 @@ if __name__ == "__main__":
                    style="text-decoration: none; color: #007bff;">hello@predata.ai</a>
             </div>
             """)
+    return demo
+
+# MAIN
+if __name__ == "__main__":
+    logging.info("Loading environment...")
+    Config.load_environment(".", "genz.dev1")
+    
+    logging.info("Environment loaded. Printing environment:")
+    Config.print_environment()
+
+    logging.info("Initializing processor_llm...")
+    processor_llm = get_processor_llm_instance()
+    logging.info("processor_llm initialized.")
+
+    logging.info("Initializing respondent_agent_llm...")
+    respondent_agent_llm = get_respondent_agent_llm_instance()
+    logging.info("respondent_agent_llm initialised.")
+    
+    # Load all user profiles from the Excel file
+    data_dictionary                = DataDictionary.generate_dictionary(Config.data_dictionary_file)
+    logging.info(f"Generated data dictionary: {data_dictionary}")
+    personality_assessment         = PVAssessment.generate_personality_assessment(Config.personality_question_file)
+    logging.info(f"Generated personality_assessment: {data_dictionary}")
+    respondent_agent_user_profiles = UserProfile.read_user_profiles_from_excel(Config.respondent_details_file, data_dictionary, personality_assessment)
+
+    # Create respondent agents for all profiles
+    respondent_agents_dict = {
+        profile.get_field("Demographics", "Name"): RespondentAgent.create(
+            profile, f"{Config.config_dir}/fastfacts/{profile.ID}_fast_facts.xlsx", respondent_agent_llm
+        )
+        for profile in respondent_agent_user_profiles[:5]
+    }
             
+    def chatbot_interface(message, history=None):
+        """
+        Handles chatbot interaction. Can be used both in Gradio and from MAIN.
+        """
+        if history is None:
+            history = []  # Ensure history is initialized
+
+        responses = ask_interview_question(respondent_agents_dict, message, processor_llm)
+        logging.info(f"Interview response is {responses}")
+
+        # Ensure responses is always a list
+        if not isinstance(responses, list):
+            responses = [responses]  # Wrap single response in a list
+        
+        # Format each response properly
+        formatted_responses = []
+        for r in responses:
+            formatted_responses.append({"role": "assistant", "content": str(r)})
+        
+        # Append user message and formatted responses to history
+        history.append({"role": "user", "content": message})
+        history.extend(formatted_responses)  # Add each response separately
+
+        logging.info(f"Return history: {history}")
+        return history, ""
+
+    demo = build_interface()
     demo.launch(debug=True)