feedback_generation_function.py

import openai
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
import faiss
from sentence_transformers import SentenceTransformer
import time
encoder = SentenceTransformer('all-mpnet-base-v2')
from sklearn.metrics import classification_report
from functools import reduce
import json

print ("finished import")
openai.api_key = os.environ["OPENAI_API_KEY"]
# MODEL_NAME = 'gpt-3.5-turbo-0613' # GPT 4 alternative: 'gpt-4-0613'
MODEL_NAME = 'gpt-4-1106-preview'
MAX_RETRIES = 5
all_strategy = pd.read_csv("all_strategy_with_generated_reason.csv")

def find_knn(target_utterance, search_inventory, top_k):
    all_utterances = search_inventory['message_text'].tolist()
    all_utterances_embeddings = encoder.encode(all_utterances)
    print (all_utterances_embeddings.shape)
    vec_dimension = all_utterances_embeddings.shape[1]
    index = faiss.IndexFlatL2(vec_dimension)
    faiss.normalize_L2(all_utterances_embeddings)
    index.add(all_utterances_embeddings)

    search_vec = encoder.encode(target_utterance)
    _vector = np.array([search_vec])
    faiss.normalize_L2(_vector)

    k = index.ntotal
    distances, ann = index.search(_vector, k=k)

    results = pd.DataFrame({'distances': distances[0], 'ann': ann[0]})
    # print (results.head(5))
    select_ind = results.ann[:top_k].to_list()
    distance_list = results.distances[:top_k].to_list()
    
    return search_inventory.iloc[select_ind], distance_list

def find_similar_situation(target_situation, list_of_situations, top_k):
    situation_embeddings = encoder.encode(list_of_situations)
    vec_dimension = situation_embeddings.shape[1]
    index = faiss.IndexFlatL2(vec_dimension)
    faiss.normalize_L2(situation_embeddings)
    index.add(situation_embeddings)

    search_vec = encoder.encode(target_situation)
    _vector = np.array([search_vec])
    faiss.normalize_L2(_vector)

    k = index.ntotal
    distances, ann = index.search(_vector, k=k)

    results = pd.DataFrame({'distances': distances[0], 'ann': ann[0]})

    # print (results.head(top_k))
    # for i in range(top_k):
    #     print (results.ann[i], all_utterances[results.ann[i]])

    select_ind = results.ann[:top_k].to_list()
    return [list_of_situations[i] for i in select_ind]        

def generate_knn_demonstrations(all_strategy, mode,  target_situation, category, u, strategy, top_k_situation, top_k_utterances):
    all_convo = all_strategy['situation'].drop_duplicates().tolist()
    if mode == 'similar_situation_knn':
        # other_convo = all_strategy[all_strategy.conversation_id != convo_id]['context'].drop_duplicates().tolist()
        # target_situation = all_strategy[(all_strategy.conversation_id == convo_id)]['context'].reset_index(drop=True)[0]
        situation_list = find_similar_situation(target_situation, all_convo, top_k_situation)
        # print (situation_list)
        utterances_in_situations = all_strategy[all_strategy['situation'].isin(situation_list)]
    elif mode == 'in_category_knn':
        utterances_in_situations = all_strategy[all_strategy.category == category].reset_index(drop=True)
    elif mode == 'in_category_similar_situation_knn':
        # other_convo_in_category = all_strategy[all_strategy.category == category]['context'].drop_duplicates().tolist()
        # target_situation = all_strategy[(all_strategy.conversation_id == convo_id)]['context'].reset_index(drop=True)[0]
        situation_list = find_similar_situation(target_situation, all_convo, top_k_situation)
        utterances_in_situations = all_strategy[all_strategy['situation'].isin(situation_list)]
    elif mode == 'all_utterances_knn':
        utterances_in_situations = all_strategy

    strategy_label = 'label_' + strategy
    suggestion_column = 'suggestion_' + strategy
    reason_column = 'reason_' + strategy
    rewrite_column = 'rewrite_' + strategy
    if strategy in ['describe', 'express', 'assert', 'reinforce', 'negotiate']:
        utterances_strong = utterances_in_situations[utterances_in_situations[strategy_label] == 'Strong'].reset_index(drop=True)
        strong_demonstrations, strong_distance = find_knn(u, utterances_strong, top_k_utterances)
        strong_demonstrations = strong_demonstrations.reset_index(drop=True)
        utterances_weak = utterances_in_situations[utterances_in_situations[strategy_label] == 'Weak'].reset_index(drop=True)
        weak_demonstrations, weak_distance = find_knn(u, utterances_weak, top_k_utterances)
        weak_demonstrations = weak_demonstrations.reset_index(drop=True)
        utterances_no = utterances_in_situations[utterances_in_situations[strategy_label] == 'No'].reset_index(drop=True)
        none_demonstrations, none_distance = find_knn(u, utterances_no, top_k_utterances)
        none_demonstrations = none_demonstrations.reset_index(drop=True)


        list_strong = [(strong_demonstrations['situation'][i], strong_demonstrations['message_id'][i], strong_demonstrations['message_text'][i], strong_demonstrations[suggestion_column][i], strong_demonstrations[reason_column][i], strong_demonstrations[rewrite_column][i]) for i in range(len(strong_demonstrations))] #ADD
        list_weak = [(weak_demonstrations['situation'][i], weak_demonstrations['message_id'][i], weak_demonstrations['message_text'][i], weak_demonstrations[suggestion_column][i], weak_demonstrations[reason_column][i], weak_demonstrations[rewrite_column][i]) for i in range(len(weak_demonstrations))] #ADD
        list_none = [(none_demonstrations['situation'][i], none_demonstrations['message_id'][i], none_demonstrations['message_text'][i], none_demonstrations[suggestion_column][i], none_demonstrations[reason_column][i], none_demonstrations[rewrite_column][i]) for i in range(len(none_demonstrations))] #ADD
      # print (strong_demonstrations['message_text'], weak_demonstrations['message_text'], none_demonstrations['message_text'])

        all_distance = strong_distance + weak_distance + none_distance
        return list_strong, list_weak, list_none, all_distance

    elif strategy in ['mindful', 'confident']:
        utterances_strong = utterances_in_situations[utterances_in_situations[strategy_label] == 'Yes'].reset_index(drop=True)
        strong_demonstrations, strong_distance = find_knn(u, utterances_strong, top_k_utterances)
        strong_demonstrations = strong_demonstrations.reset_index(drop=True)
        utterances_weak = utterances_in_situations[utterances_in_situations[strategy_label] == 'No'].reset_index(drop=True)
        weak_demonstrations, weak_distance = find_knn(u, utterances_weak, top_k_utterances)
        weak_demonstrations = weak_demonstrations.reset_index(drop=True)
        # return strong_demonstrations, weak_demonstrations
        list_strong = [(strong_demonstrations['situation'][i], strong_demonstrations['message_id'][i], strong_demonstrations['message_text'][i], strong_demonstrations[suggestion_column][i], strong_demonstrations[reason_column][i], strong_demonstrations[rewrite_column][i]) for i in range(len(strong_demonstrations))] # ADD
        list_weak = [(weak_demonstrations['situation'][i], weak_demonstrations['message_id'][i], weak_demonstrations['message_text'][i], weak_demonstrations[suggestion_column][i], weak_demonstrations[reason_column][i], weak_demonstrations[rewrite_column][i]) for i in range(len(weak_demonstrations))] # ADD

        all_distance = strong_distance + weak_distance 
        return list_strong, list_weak, '', all_distance

def form_prompt_from_demonstrations(list_strong, list_weak, list_none, strategy, prompt_strategy, include_improve, cot_dict, zip_reorder=True):
    # list_strong: 
    # (strong_demonstrations['context'][i], strong_demonstrations['message_id'][i], strong_demonstrations['message_text'][i], strong_demonstrations[suggestion_column][i], strong_demonstrations[reason_column][i])
    strategy_cap = strategy[0].upper() + strategy[1:]

    if strategy == 'describe':
        strong_improve_str = f"This is a great {strategy_cap}! It sticks to the facts, makes no judgemental statements, and is objective."
    elif strategy == 'express':
        strong_improve_str = f"This is a great {strategy_cap}! It express your feeling or opinions explicitly." 
    elif strategy == 'assert':
        strong_improve_str = f"This is a great {strategy_cap}! It is clear, concise, and to the point." 
    elif strategy == 'reinforce':
        strong_improve_str = f"This is a great {strategy_cap}! It reinforces the other person." 
    elif strategy == 'negotiate':
        strong_improve_str = f"This is a great {strategy_cap}! It shows that you are trying to find an alternative solution." 
    elif strategy == 'mindful':
        strong_improve_str = f"This utterance shows mindfulness. You focused on your goal, and did not get distracted or get off topic."
    elif strategy == 'confident':
        strong_improve_str = f"This utterance shows confidence. You used a confident tone. The statement was effective and competent."
    
    # weak_improve = [list(weak_demonstrations[suggestion_column])[i] for i in range(actual_top_k_weak)]
    weak_improve = [list_weak[i][3] for i in range(len(list_weak))]
    
    if strategy in ['describe', 'express', 'assert', 'reinforce', 'negotiate']:
        # none_improve = [list(none_demonstrations[suggestion_column])[i] for i in range(actual_top_k_none)]
        none_improve = [list_none[i][3] for i in range(len(list_none))]

    strong_rating = f"Strong {strategy_cap}"
    weak_rating = f"Weak {strategy_cap}"
    if strategy in ['describe', 'express', 'assert', 'reinforce', 'negotiate']:
        strong_rating = f"Strong {strategy_cap}"
        weak_rating = f"Weak {strategy_cap}"
        none_rating = f"No {strategy_cap}"
    elif strategy in ['mindful', 'confident']:
        strong_rating = "Yes"
        weak_rating = "No"

    
    user_prompt = ''
    if prompt_strategy == 'CoT':
        if strategy in ['mindful', 'confident']:
            if strategy == 'mindful':  answer_subquestion_yes, answer_subquestion_no = 'No', 'Yes'
            elif strategy == 'confident': answer_subquestion_yes, answer_subquestion_no = 'Yes', 'No'
            for i in range(len(list_strong)):
                if include_improve:
                    user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {cot_dict[strategy][0]}{answer_subquestion_yes}, {strategy_cap} Rating: {strong_rating}, Suggestion for improvement: {strong_improve_str}\n"
                else:
                    user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {cot_dict[strategy][0]}{answer_subquestion_yes}, {strategy_cap} Rating: {strong_rating}\n"
            for i in range(len(list_weak)):
                if include_improve:
                    user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {cot_dict[strategy][0]}{answer_subquestion_no},  {strategy_cap} Rating: {weak_rating}, Suggestion for improvement: {list_weak[i][3]}\n"
                else:
                    user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {cot_dict[strategy][0]}{answer_subquestion_no},  {strategy_cap} Rating: {weak_rating}\n"
        elif strategy in ['describe', 'express', 'assert', 'reinforce', 'negotiate']:
            for i in range(len(list_strong)):
                if include_improve:
                    user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {cot_dict[strategy][0]}Yes, {cot_dict[strategy][1]}Yes, {strategy_cap} Rating: {strong_rating}, Suggestion for improvement: {strong_improve_str}\n"
                else:
                    user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {cot_dict[strategy][0]}Yes, {cot_dict[strategy][1]}Yes, {strategy_cap} Rating: {strong_rating}\n"
            for i in range(len(list_weak)):
                if include_improve:
                    user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {cot_dict[strategy][0]}Yes, {cot_dict[strategy][1]}No, {strategy_cap} Rating: {weak_rating}, Suggestion for improvement: {list_weak[i][3]}\n"
                else:
                    user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {cot_dict[strategy][0]}Yes, {cot_dict[strategy][1]}No, {strategy_cap} Rating: {weak_rating}\n"
            for i in range(len(list_none)):
                if include_improve:
                    user_prompt += f"Context: {list_none[i][0]}, Utterance: {list_none[i][2]}, {cot_dict[strategy][0]}No, {strategy_cap} Rating: {none_rating}, Suggestion for improvement: {list_none[i][3]}\n" # TODO: one question or two questions for None situation?n 
                else:
                    user_prompt += f"Context: {list_none[i][0]}, Utterance: {list_none[i][2]}, {cot_dict[strategy][0]}No, {strategy_cap} Rating: {none_rating}\n" 


    elif prompt_strategy == 'CoT-reason':
        if strategy in ['mindful', 'confident']:
            if strategy == 'mindful':  answer_subquestion_yes, answer_subquestion_no = 'No', 'Yes'
            elif strategy == 'confident': answer_subquestion_yes, answer_subquestion_no = 'Yes', 'No'
            for i in range(len(list_strong)):
                if include_improve:
                    user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {cot_dict[strategy][0]}{answer_subquestion_yes}, Reason for rating: {list_strong[i][4]}, {strategy_cap} Rating: {strong_rating}, Suggestion for improvement: {strong_improve_str}###"
                else:
                    user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {cot_dict[strategy][0]}{answer_subquestion_yes}, Reason for rating: {list_strong[i][4]}, {strategy_cap} Rating: {strong_rating}\n"
            for i in range(len(list_weak)):
                if include_improve:
                    user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {cot_dict[strategy][0]}{answer_subquestion_no},  Reason for rating: {list_weak[i][4]}, {strategy_cap} Rating: {weak_rating}, Suggestion for improvement: {list_weak[i][3]}###"
                else:
                    user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {cot_dict[strategy][0]}{answer_subquestion_no},  Reason for rating: {list_weak[i][4]}, {strategy_cap} Rating: {weak_rating}\n"
        elif strategy in ['describe', 'express', 'assert', 'reinforce', 'negotiate']:
            if include_improve:
                strong_prompts_list = [f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {cot_dict[strategy][0]}Yes, {cot_dict[strategy][1]}Yes, Reason for rating: {list_strong[i][4]}, {strategy_cap} Rating: {strong_rating}, Suggestion for improvement: {strong_improve_str}###" for i in range(len(list_strong))]
                strong_prompts = ''.join(strong_prompts_list)

                weak_prompts_list = [f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {cot_dict[strategy][0]}Yes, {cot_dict[strategy][1]}No, Reason for rating: {list_weak[i][4]}, {strategy_cap} Rating: {weak_rating}, Suggestion for improvement: {list_weak[i][3]}###" for i in range(len(list_weak))]
                weak_prompts = ''.join(weak_prompts_list)

                none_prompts_list = [f"Context: {list_none[i][0]}, Utterance: {list_none[i][2]}, {cot_dict[strategy][0]}No, Reason for rating: {list_none[i][4]}, {strategy_cap} Rating: {none_rating}, Suggestion for improvement: {list_none[i][3]}###" for i in range(len(list_none))]
                none_prompts = ''.join(none_prompts_list)

                # if zip_reorder:
                user_prompt_list = []
                for s,w,n in zip(strong_prompts_list, weak_prompts_list, none_prompts_list):
                    user_prompt_list.extend([s,w,n])

                user_prompt = ''.join(user_prompt_list)
                # else:
                # user_prompt = strong_prompts + weak_prompts + none_prompts
 
    elif prompt_strategy == 'reason':
            if strategy in ['mindful', 'confident']:
                if strategy == 'mindful':  answer_subquestion_yes, answer_subquestion_no = 'No', 'Yes'
                elif strategy == 'confident': answer_subquestion_yes, answer_subquestion_no = 'Yes', 'No'
                if include_improve:
                    strong_prompts_list = [f"Context: {list_strong[i][0]} Utterance: {list_strong[i][2]} Step 1 - Reason for rating: {list_strong[i][4]}### Step 2 - {strategy_cap} Rating: {strong_rating}### Step 3 - Suggestion for improvement: {strong_improve_str}###" for i in range(len(list_strong))]
                    strong_prompts = ''.join(strong_prompts_list)

                    weak_prompts_list = [f"Context: {list_weak[i][0]} Utterance: {list_weak[i][2]} Step 1 - Reason for rating: {list_weak[i][4]}### Step 2 - {strategy_cap} Rating: {weak_rating}### Step 3 - Suggestion for improvement: {list_weak[i][3]}###" for i in range(len(list_weak))]
                    weak_prompts = ''.join(weak_prompts_list)

                    # none_prompts_list = [f"Context: {list_none[i][0]}, Utterance: {list_none[i][2]}, Reason for rating: {list_none[i][4]}, {strategy_cap} Rating: {none_rating}, Suggestion for improvement: {list_none[i][3]}###" for i in range(len(list_none))]
                    # none_prompts = ''.join(none_prompts_list)

                    if zip_reorder:
                        # if zip_reorder:
                        user_prompt_list = []
                        for s,w in zip(strong_prompts_list, weak_prompts_list):
                            user_prompt_list.extend([s,w])
                        user_prompt = ''.join(user_prompt_list)
                    else:
                        user_prompt = strong_prompts + weak_prompts + none_prompts

            elif strategy in ['describe', 'express', 'assert', 'reinforce', 'negotiate']:
                if include_improve:
                    # print ("right prompt construction")
                    strong_prompts_list = [f"Context: {list_strong[i][0]} Utterance: {list_strong[i][2]} Step 1 - Reason for rating: {list_strong[i][4]}### Step 2 - {strategy_cap} Rating: {strong_rating}### Step 3 - Suggestion for improvement: {strong_improve_str}###" for i in range(len(list_strong))]
                    strong_prompts = ''.join(strong_prompts_list)

                    weak_prompts_list = [f"Context: {list_weak[i][0]} Utterance: {list_weak[i][2]} Step 1 - Reason for rating: {list_weak[i][4]}### Step 2 - {strategy_cap} Rating: {weak_rating}### Step 3 -Suggestion for improvement: {list_weak[i][3]}###" for i in range(len(list_weak))]
                    weak_prompts = ''.join(weak_prompts_list)

                    none_prompts_list = [f"Context: {list_none[i][0]} Utterance: {list_none[i][2]} Step 1 - Reason for rating: {list_none[i][4]}### Step 2 -{strategy_cap} Rating: {none_rating}### Step 3 -Suggestion for improvement: {list_none[i][3]}###" for i in range(len(list_none))]
                    none_prompts = ''.join(none_prompts_list)

                    # print (len(strong_prompts_list), len(weak_prompts_list), len(none_prompts_list))

                    if zip_reorder:
                        # if zip_reorder:
                        user_prompt_list = []
                        for s,w,n in zip(strong_prompts_list, weak_prompts_list, none_prompts_list):
                            user_prompt_list.extend([s,w,n])

                        user_prompt = ''.join(user_prompt_list)
                    else:
                        user_prompt = strong_prompts + weak_prompts + none_prompts
    # elif prompt_strategy == 'reason':
    #     if strategy in ['mindful', 'confident']:
    #         if strategy == 'mindful':  answer_subquestion_yes, answer_subquestion_no = 'No', 'Yes'
    #         elif strategy == 'confident': answer_subquestion_yes, answer_subquestion_no = 'Yes', 'No'
    #         for i in range(len(list_strong)):
    #             if include_improve:
    #                 user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, Reason for rating: {list_strong[i][4]}, {strategy_cap} Rating: {strong_rating}, Suggestion for improvement: {strong_improve_str}###"
    #             else:
    #                 user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, Reason for rating: {list_strong[i][4]}, {strategy_cap} Rating: {strong_rating}\n"
    #         for i in range(len(list_weak)):
    #             if include_improve:
    #                 user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, Reason for rating: {list_weak[i][4]}, {strategy_cap} Rating: {weak_rating}, Suggestion for improvement: {list_weak[i][3]}###"
    #             else:
    #                 user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, Reason for rating: {list_weak[i][4]}, {strategy_cap} Rating: {weak_rating}\n"
    #     elif strategy in ['describe', 'express', 'assert', 'reinforce', 'negotiate']:
    #         for i in range(len(list_strong)):
    #             if include_improve:
    #                 user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, Reason for rating: {list_strong[i][4]}, {strategy_cap} Rating: {strong_rating}, Suggestion for improvement: {strong_improve_str}###"
    #             else:
    #                 user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, Reason for rating: {list_strong[i][4]}, {strategy_cap} Rating: {strong_rating}\n"
    #         for i in range(len(list_weak)):
    #             if include_improve:
    #                 user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, Reason for rating: {list_weak[i][4]}, {strategy_cap} Rating: {weak_rating}, Suggestion for improvement: {list_weak[i][3]}###"
    #             else:
    #                 user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, Reason for rating: {list_weak[i][4]}, {strategy_cap} Rating: {weak_rating}\n"
    #         for i in range(len(list_none)):
    #             if include_improve:
    #                 user_prompt += f"Context: {list_none[i][0]}, Utterance: {list_none[i][2]}, Reason for rating: {list_none[i][4]}, {strategy_cap} Rating: {none_rating}, Suggestion for improvement: {list_none[i][3]}###" # TODO: one question or two questions for None situation?n 
    #             else:
    #                 user_prompt += f"Context: {list_none[i][0]}, Utterance: {list_none[i][2]}, Reason for rating: {list_none[i][4]}, {strategy_cap} Rating: {none_rating}\n" 

    else:
        for i in range(len(list_strong)):
            if include_improve:
                user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {strategy_cap} Rating: {strong_rating}, Suggestion for improvement: {strong_improve_str}###"
            else:
                user_prompt += f"Context: {list_strong[i][0]}, Utterance: {list_strong[i][2]}, {strategy_cap} Rating: {strong_rating}\n"
        for i in range(len(list_weak)):
            if include_improve:
                user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {strategy_cap} Rating: {weak_rating}, Suggestion for improvement: {list_weak[i][2]}###"
            else:
                user_prompt += f"Context: {list_weak[i][0]}, Utterance: {list_weak[i][2]}, {strategy_cap} Rating: {weak_rating}\n"
        if strategy in ['describe', 'express', 'assert', 'reinforce', 'negotiate']:
            for i in range(len(list_none)):
                if include_improve:
                    user_prompt += f"Context: {list_none[i][0]}, Utterance: {list_none[i][2]}, {strategy_cap} Rating: {none_rating}, Suggestion for improvement: {list_none[i][3]}###"
                else:
                    user_prompt += f"Context: {list_none[i][0]}, Utterance: {list_none[i][2]}, {strategy_cap} Rating: {none_rating}\n"
    print(user_prompt)
    return user_prompt

def generate_feedback(current_situation, category, u, prompt_strategy, prompt_selection, strategy_list):
    # parse strategy
    strategy = strategy_list[0]
    strategy = strategy[0].lower() + strategy[1:]
    print (f"Generating feedback for {strategy}")

    # parse category
    cate = category[0].lower() + category[1:]


    # load files once, move to user_study_interface.py
    with open('prompts.json', 'r') as fp:
        system_prompts = json.load(fp)

    print ("There are in total {} utterances".format(len(all_strategy)))
 
    list_of_convo = all_strategy['conversation_id'].drop_duplicates().tolist()
    print (list_of_convo)
    all_convo_results = pd.DataFrame()

    dict_CoT = {
        'describe': ['Answer in Yes or No: The utterance is or contains a description of the given context. Answer:', 'Answer in Yes or No: The utterance sticks to the fact, makes no judgemental statement, and is objective. Answer:'],
        'express': ['Answer in Yes or No: The utterance is or contains an expression of the speaker\'s feelings. Answer:', 'Answer in Yes or No: The expression of feelings in the utterance is clear and explicit. Answer:'],
        'assert': ['Answer in Yes or No: The utterance is or contains an ask for what the speaker wants. Answer:', 'Answer in Yes or No: The ask in the utterance is clear and explicit, or the speaker is saying no clearly and explicitly. Answer:'],
        'reinforce': ['Answer in Yes or No: The utterance is or contains a reinforcement of a reward for the other person. Answer:', 'Answer in Yes or No: The reinforcement in the utterance is targeted to the other person and is communicated clearly. Answer:'],
        'negotiate': ['Answer in Yes or No: The utterance offers a compromise or an alternative solution. Answer:', 'Answer in Yes or No: The compromise or alternative solution in the utterance is clear and explicit. Answer:'], # TODO: add weak description from clusters
        'mindful':['Answer in Yes or No: The utterance shows that the speaker is responding to attacks and criticism or is losing track of their goals. Answer:'], # REVERSE!
        'confident': ['Answer in Yes or No: The utterance shows a confident tone, and is effective and competent in conveying the speaker\'s goal. Answer:'], 
    }


    system_prompt = system_prompts[prompt_strategy][strategy]
 
    start_time = time.time()
    current_tries = 1
    strategy_cap = strategy[0].upper() + strategy[1:]

    # User Prompt
    # if prompt_selection == "knn":
    #     user_prompt = get_knn_prompt(u, top_k, True, index_knn, args.strategy, prompt_strategy, dict_CoT)
    if prompt_selection in ("in_category_similar_situation_knn", "similar_situation_knn", "in_category_knn", "all_utterances_knn") :
        print ("right")
        strong_, weak_, none_, knn_distance = generate_knn_demonstrations(all_strategy, prompt_selection, current_situation, cate, u, strategy, top_k_situation=5, top_k_utterances=3) #all_strategy, mode,  target_situation, category, u, strategy, top_k_situation, top_k_utterances
        user_prompt = form_prompt_from_demonstrations(strong_, weak_, none_, strategy, prompt_strategy, True, dict_CoT)

    elif prompt_selection == "zero_shot":
        user_prompt = ''

    # Current Prompt
    if prompt_strategy == "CoT" or prompt_strategy == "CoT-reason" :
        current_prompt = f"Context: {current_situation}, Utterance: {u},"
    elif prompt_strategy == "reason":
        current_prompt = f"Context: {current_situation}, Utterance: {u}, Reason for rating:"
    else:
        current_prompt = f"Context: {current_situation}, Utterance: {u}, {strategy_cap} Rating:"
        
    prompt = [{"role":"system", "content":system_prompt}, {"role":"user", "content": user_prompt+current_prompt}]

    print ("Full prompt: ", prompt)
    while current_tries <= MAX_RETRIES:
        try:
            response = openai.ChatCompletion.create(
            model=MODEL_NAME,
            messages = prompt,
            # max_tokens = 256,
            temperature = 0,
            ) 

            curr_response_str = response['choices'][0]['message']['content'].replace('\n', ' ').strip()
            # print (curr_response_str, '\t')

            break
        except Exception as e:
            print('error: ', str(e))
            print('response retrying')
            current_tries += 1
            if current_tries > MAX_RETRIES:
                break
            time.sleep(5)
    time_taken = time.time() - start_time
    print (f'Time taken for this utterance: {time_taken}')
    try:
        rating_list = curr_response_str.split(f"{strategy_cap} Rating: ", 1)[1].split(" ", 2)[:2].replace('[,.#]', '', regex=True)
        rating = ' '.join(rating_list)
        suggestion = curr_response_str.split(f"{strategy_cap} Rating: ", 1)[1].split(" ", 2)[2:][0].split("###", 1)[0]
        output = rating + '. \n' + suggestion
    except:
        output = curr_response_str
    
    return output

# def generate_feedback_with_mc(current_situation, category, u, prompt_strategy, prompt_selection, strategy_list):
#     strategy_output = generate_feedback(current_situation, category, u, prompt_strategy, prompt_selection, strategy_list)
#     mindful_output = generate_feedback(current_situation, category, u, prompt_strategy, prompt_selection, ['mindful'])
#     confident_output = generate_feedback(current_situation, category, u, prompt_strategy, prompt_selection, ['confident'])

#     print (strategy_output, mindful_output, confident_output)
#     all_concat = strategy_output + '\n' + mindful_output + '\n' + confident_output

#     return all_concat

def generate_feedback_with_mc(current_situation, category, u, prompt_strategy, prompt_selection, strategy_list):
    try:
        strategy_output = generate_feedback(current_situation, category, u, prompt_strategy, prompt_selection, strategy_list)
        strategy = strategy_list[0]
        strategy_rating = strategy_output.split(f'{strategy} Rating:')[1].split('###')[0].strip()
        strategy_suggestion = strategy_output.split(f'{strategy} Rating:')[1].split('Suggestion for improvement: ')[1].split('###')[0].strip()

        strategy_feedback =  strategy_rating + '<br/>' + strategy_suggestion
    except:
        strategy_feedback = 'The model does not have a feedback for this utterance. Please try a slightly different wording.'
    
    try:
        mindful_output = generate_feedback(current_situation, category, u, prompt_strategy, prompt_selection, ['mindful'])
        mindful_rating = mindful_output.split('Mindful Rating:')[1].split('###')[0].strip()

        if mindful_rating != 'Yes' and mindful_rating != 'No':
            mindful_feedback = ''
        elif mindful_rating == 'Yes':
            mindful_feedback = "Mindfulness check: Well done! &#128077; "
        else:
            mindful_feedback = "Mindfulness check: " + mindful_output.split('Mindful Rating:')[1].split('Suggestion for improvement: ')[1].split('###')[0].strip()

    except:
        mindful_feedback = ''
    try: 
        confident_output = generate_feedback(current_situation, category, u, prompt_strategy, prompt_selection, ['confident'])
        confident_rating = confident_output.split('Confident Rating:')[1].split('###')[0].strip()

        if confident_rating != 'Yes' and confident_rating != 'No':
            confident_feedback = ''
        elif confident_rating == 'Yes':
            confident_feedback = "Confidence check: Well done! &#128077; "
        else:
            confident_feedback = "Confidence check: " + confident_output.split('Confident Rating:')[1].split('Suggestion for improvement: ')[1].split('###')[0].strip()
    except:
        confident_feedback = ''
            
    print (strategy_output, mindful_output, confident_output)
    all_concat = strategy_feedback + '</br> </br>' + mindful_feedback + '</br>' + confident_feedback

    return all_concat   

def generate_skill_suggestion(situation, input_history, demonstration_mode):

    # format of input_history: 
    #  [{"role":"system", "content": system_input}] + input_history + [{"role":"user", "content":new_input}]
    #

    if len(input_history) == 0:
        return "Describe", "You can start the conversation by describing the situation."
    if input_history[-1]['role'] == 'system':
        previous_partner_message = input_history[-1]['content']
        previous_client_message = input_history[-2]['content']
    else:
        previous_partner_message = input_history[-2]['content']
        previous_client_message = input_history[-1]['content']
    prompt_other_message = f'Conversation context: {situation}. I received the response: "{previous_partner_message}" What DEAR MAN strategy should I use next?' 

    prompt = prompt_other_message
    system_prompt_one = "Please suggest the top DEAR MAN skill to use in the next response: describe, express, assert, reinforce, negotiate. In the first line, output only the name of the skill immediately. In the second line, suggestion reason why use this skill. Address the reason in second person perspective. Separate the lines with ###. YOU MUST FOLLOW THIS FORMAT." 
    prompt = [{"role":"system", "content":system_prompt_one}, {"role":"user", "content": prompt_other_message}]

    current_tries=0
    while current_tries <= MAX_RETRIES:
        try:
            response = openai.ChatCompletion.create(
            model=MODEL_NAME,
            messages = prompt,
            max_tokens = 256,
            temperature = 0,
            ) 
            # print (prompt)k
            curr_response_str = response['choices'][0]['message']['content'].replace('\n', ' ').strip()
            print (curr_response_str, '\t')
            # print ('Suggestion: ', all_strategy[f'suggestion_{strategy}'][m], '\t')
            # model_output.append(curr_response_str)
            break
        except Exception as e:
            print('error: ', str(e))
            print('response retrying')
            current_tries += 1
            if current_tries > MAX_RETRIES:
                break
            time.sleep(5)

    # suggested_skills = curr_response_str.split('###')[0].split(',')
    # suggested_skills = [skill.strip() for skill in suggested_skills]
    # suggested_skills = [skill[0].upper() + skill[1:].lower() for skill in suggested_skills]
    # suggest_reason = curr_response_str.split('###')[1:]
    # # convert suggest_reason to a string
    # suggest_reason_str = ' '.join(suggest_reason)
    # print (suggest_reason, suggest_reason_str)
    # if len(suggested_skills) == 2:
    #     print (suggested_skills[0], suggested_skills[1])
    #     return suggested_skills[0], suggested_skills[1], suggest_reason_str
            
    suggested_skills = curr_response_str.split('###')[0].split(',')
    suggested_skills = [skill.strip() for skill in suggested_skills]
    suggested_skills = [skill[0].upper() + skill[1:].lower() for skill in suggested_skills]
    suggest_reason = curr_response_str.split('###')[1:]
    suggest_reason_str = ' '.join(suggest_reason)
    print (suggested_skills, suggest_reason_str)
    if len(suggested_skills) == 1:
        return suggested_skills[0], suggest_reason_str
    else: 
        print (suggested_skills)
        print ("Suggestion format is wrong")
        return 'Describe', "During the conversation, it is helpful to reground the fact."