feat: Add ideal answer

app.py

@@ -11,6 +11,7 @@ from constants import JOB_DESCRIPTION, RESUME_TEXT
 API_KEY = os.getenv("API_KEY")
 CONVERSATIONAL_MODEL_API_ENDPOINT = "https://talent-interview-prep-conversational-model.multimodal.dev/"
 QUESTION_RELATED_FEEDBACK_API_ENDPOINT = "https://talent-interview-prep-question-related-feedback.multimodal.dev/"
+QUESTION_RELATED_IDEAL_ANSWER_API_ENDPOINT = "https://talent-interview-prep-question-related-ideal-answer.multimodal.dev/"
 CONVERSATIONAL_MODEL_FEEDBACK_API_ENDPOINT = "https://talent-interview-prep-conversation-feedback.multimodal.dev/"
 
 # Predefined questions
@@ -33,6 +34,8 @@ first_question_selected = False
 
 current_turns = 0
 
+interview_data_with_feedback = []
+
 def chatbot_api_call(interview_question, user_input, conversation_mode, conversation_turns_limit, include_company_name, include_resume_text):
     global session_id
     print(f"Calling API with session_id: {session_id}")  
@@ -81,7 +84,7 @@ def remove_html_tags(text):
     cleaned_text = soup.get_text().strip('"').replace('\\"', '"')
     return cleaned_text
     
-def question_related_feedback_api_call(interview_conversation, feedback_type="standard"):
+def question_related_feedback_api_call(interview_data, feedback_type="standard"):
     headers = {
         "x-api-key": API_KEY,
         "Content-Type": "application/json"
@@ -89,7 +92,7 @@ def question_related_feedback_api_call(interview_conversation, feedback_type="st
 
     data = {
         "job_title": "Senior Product Manager",
-        "interview_conversation": interview_conversation,
+        "interview_data": interview_data,
         "feedback_type": feedback_type
     }
 
@@ -105,7 +108,31 @@ def question_related_feedback_api_call(interview_conversation, feedback_type="st
         print(f"Error: {str(e)}")
         return "Error: Unable to reach the API or invalid response received."
     
-def conversational_model_feedback_api_call(interview_conversation, feedback_type="standard"):
+def question_related_ideal_answer_api_call(interview_data_with_feedback, feedback_type="standard"):
+    headers = {
+        "x-api-key": API_KEY,
+        "Content-Type": "application/json"
+    }
+
+    data = {
+        "job_title": "Senior Product Manager",
+        "interview_data_with_feedback": interview_data_with_feedback,
+        "feedback_type": feedback_type
+    }
+
+    try:
+        response = requests.post(QUESTION_RELATED_IDEAL_ANSWER_API_ENDPOINT, headers=headers, json=data)
+        
+        if response.status_code == 200:
+            return response.text
+        else:
+            return f"Error: Received status code {response.status_code} from the API"
+    
+    except Exception as e:
+        print(f"Error: {str(e)}")
+        return "Error: Unable to reach the API or invalid response received."
+    
+def conversational_model_feedback_api_call(interview_data, feedback_type="standard"):
     headers = {
         "x-api-key": API_KEY,
         "Content-Type": "application/json"
@@ -113,7 +140,7 @@ def conversational_model_feedback_api_call(interview_conversation, feedback_type
 
     data = {
         "job_title": "Senior Product Manager",
-        "interview_conversation": interview_conversation,
+        "interview_data": interview_data,
         "feedback_type": feedback_type
     }
 
@@ -137,9 +164,10 @@ def enable_send_button(message, selected_question):
     return gr.update(interactive=False), gr.update(label="Choose an interview question (Required)")
 
 def handle_question_change(history, selected_question, conversation_mode):
-    global session_id, first_question_selected, current_turns  
+    global session_id, first_question_selected, current_turns, interview_data_with_feedback
 
     current_turns = 0
+    interview_data_with_feedback = []
 
     if conversation_mode == 'Interviewer':
         updated_label = f"Conversation turns: {current_turns}"
@@ -169,10 +197,11 @@ def handle_question_change(history, selected_question, conversation_mode):
         return [entry for entry in new_history if entry[1] is not None], gr.update(interactive=False), gr.update(interactive=True), gr.update(label=updated_label), feedback_box_state
 
 def reset_interface(conversation_mode):
-    global session_id, first_question_selected, current_turns
+    global session_id, first_question_selected, current_turns, interview_data_with_feedback
 
     first_question_selected = False
     current_turns = 0
+    interview_data_with_feedback = []
 
     if conversation_mode == "Interviewer":
         # chatbot_label = "Multimodal Interviewer Agent"
@@ -221,15 +250,6 @@ def create_demo():
     with gr.Blocks() as demo:
         gr.Markdown("# Talent Interview Prep - Conversational Model")
 
-        # gr.Markdown("**Some details**")
-        # gr.Markdown("""
-        # - Job title: Senior Product Manager 
-        # - Fictional company name: InnovateTech Solutions
-        # - Fictional job description [here](<link>)
-        # - Fictional candidate resume [here](<link>)
-        # - The questions are predefined and generated previously
-        # """)
-
         gr.Markdown("""### Please select a conversation mode to begin""")
 
         with gr.Row():
@@ -267,7 +287,7 @@ def create_demo():
         question_dropdown.change(fn=handle_question_change, inputs=[chatbot, question_dropdown, conversation_mode], outputs=[chatbot, send_btn, msg, chatbot, feedback_box])
 
         def respond(message, history, conversation_mode, selected_question, conversation_turns_limit, feedback_type, include_company_name, include_resume_text):
-            global current_turns
+            global current_turns, interview_data_with_feedback
 
             feedback = ""
 
@@ -278,23 +298,54 @@ def create_demo():
 
             bot_message, conversation_end_flag, chat_memory = chatbot_api_call(clean_question, message, conversation_mode, conversation_turns_limit, include_company_name, include_resume_text)
             
-            print(f"Conversation end? {conversation_end_flag}")
+            print(f"Conversation end? {conversation_end_flag}\n")
             print(f"{chat_memory=}\n")
 
-            if conversation_mode == 'Interviewer':
-                current_turns += 1
-                updated_label = f"Conversation turns: {current_turns}"
-                feedback = f"{current_turns}º conversation turn feedback\n\n{remove_html_tags(question_related_feedback_api_call(chat_memory['messages'][:-1], feedback_type.lower())).strip()}"
-            else:
-                updated_label = "Multimodal Coach Agent"
+            updated_label = f"Conversation turns: {current_turns}" if conversation_mode == 'Interviewer' else "Multimodal Coach Agent"
 
             if conversation_end_flag:
                 if conversation_mode == 'Interviewer':
-                    feedback = f"Whole conversation feedback\n\n{remove_html_tags(conversational_model_feedback_api_call(chat_memory['messages'], feedback_type.lower())).strip()}"
+                    feedback = (
+                        f"Whole conversation feedback\n\n"
+                        f"{remove_html_tags(conversational_model_feedback_api_call(chat_memory['messages'], feedback_type.lower())).strip()}"
+                    )
+                return (
+                    history + [(message, bot_message)],
+                    "",
+                    gr.update(interactive=False),
+                    gr.update(interactive=False),
+                    gr.update(label=updated_label),
+                    feedback,
+                )
 
-                return history + [(message, bot_message)], "", gr.update(interactive=False), gr.update(interactive=False), gr.update(label=updated_label), feedback
-            else:
-                return history + [(message, bot_message)], "", gr.update(interactive=True), gr.update(interactive=True), gr.update(label=updated_label), feedback
+            if conversation_mode == 'Interviewer':
+                current_turns += 1
+                interview_data = chat_memory['messages'][:-1]
+                
+                raw_feedback = question_related_feedback_api_call(interview_data, feedback_type.lower())
+                feedback = f"{current_turns}º conversation turn feedback{remove_html_tags(raw_feedback).strip()}"
+                
+                interview_data_with_feedback.extend(interview_data)
+                interview_data_with_feedback.append({"type": "feedback", "content": raw_feedback})
+
+                print(f"{interview_data_with_feedback=}\n")
+                
+                ideal_answer = question_related_ideal_answer_api_call(interview_data_with_feedback, feedback_type.lower())
+
+                bot_message += f"\n\n---"
+                bot_message += f"\n**Ideal answer:**"
+                bot_message += f"\n>{ideal_answer}"
+
+            updated_label = f"Conversation turns: {current_turns}" if conversation_mode == 'Interviewer' else "Multimodal Coach Agent"
+
+            return (
+                history + [(message, bot_message)],
+                "",
+                gr.update(interactive=True),
+                gr.update(interactive=True),
+                gr.update(label=updated_label),
+                feedback,
+            )
 
         conversation_mode.change(fn=reset_interface, inputs=conversation_mode, outputs=[chatbot, question_dropdown, msg, send_btn, conversation_turns_limit, feedback_box, feedback_type_dropdown])