import requests
import random
import time
import pandas as pd
import gradio as gr
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
import shap


def read1(lang, num_selected_former):
    if lang in ['en']:
        fname = 'data1_en.txt'
    else:
        fname = 'data1_nl_10.txt'
    with open(fname, encoding='utf-8') as f:
        content = f.readlines()
        index_selected = random.randint(0,len(content)/2-1)
        while index_selected == num_selected_former:
            index_selected = random.randint(0,len(content)/2-1)
        text = eval(content[index_selected*2])
        interpretation = eval(content[int(index_selected*2+1)])
        if lang == 'en': 
            min_len = 4
        else:
            min_len = 2

        while len(text['text'].split(' ')) <= min_len or '\\' in text['text'] or '//' in text['text']:
            index_selected = random.randint(0,len(content)/2-1)
            text = eval(content[int(index_selected*2)])
        res_tmp = [(i, 0) for i in text['text'].split(' ')]
        res = {"original": text['text'], "interpretation": res_tmp}
        
    return res, lang, index_selected

def read1_written(lang):
    if lang in ['en']:
        fname = 'data1_en.txt'
    else:
        fname = 'data1_nl_10.txt'
    with open(fname, encoding='utf-8') as f:
        content = f.readlines()
        index_selected = random.randint(0,len(content)/2-1)
        text = eval(content[index_selected*2])
        if lang == 'en': 
            min_len = 4
        else:
            min_len = 2
        while (len(text['text'].split(' '))) <= min_len or '\\' in text['text'] or '//' in text['text']:
        # while (len(text['text'].split(' '))) <= min_len:
            index_selected = random.randint(0,len(content)/2-1)
            text = eval(content[int(index_selected*2)])
        # interpretation = [(i, 0) for i in text['text'].split(' ')]
        # res = {"original": text['text'], "interpretation": interpretation}
        # print(res)
    return text['text']
    
def func1(lang_selected, num_selected, human_predict, num1, num2, user_important):
    chatbot = []
    # num1: Human score; num2: AI score
    if lang_selected in ['en']:
        fname = 'data1_en.txt'
    else:
        fname = 'data1_nl_10.txt'
    with open(fname) as f:
        content = f.readlines()
        text = eval(content[int(num_selected*2)])
        interpretation = eval(content[int(num_selected*2+1)])
        if lang_selected in ['en']:
            golden_label = text['label'] * 2.5
        else:
            golden_label = text['label'] * 10

    '''
    # (START) API version -- quick
    
    API_URL = "https://api-inference.huggingface.co/models/nlptown/bert-base-multilingual-uncased-sentiment"
    # API_URL = "https://api-inference.huggingface.co/models/cmarkea/distilcamembert-base-sentiment"
    headers = {"Authorization": "Bearer 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"}

    response = requests.post(API_URL, headers=headers, json=text['text'])
    output = response.json()
    
    # result = dict()
    star2num = {
        "5 stars": 100,
        "4 stars": 75,
        "3 stars": 50,
        "2 stars": 25,
        "1 star": 0,
    }

    print(output)
    out = output[0][0]
    # (END) API version
    ''' 

    # (START) off-the-shelf version -- slow at the beginning
    # Load model directly

    tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
    model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")

    # Use a pipeline as a high-level helper

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    print(device)
    classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, device=device)
    output = classifier([text['text']])

    star2num = {
        "5 stars": 10,
        "4 stars": 7.5,
        "3 stars": 5,
        "2 stars": 2.5,
        "1 star": 0,
    }
    print(output)
    out = output[0]
    
    # (END) off-the-shelf version
    
    ai_predict = star2num[out['label']]
    # result[label] = out['score']

    user_select = "You focused on "
    flag_select = False
    if user_important == "":
        user_select += "nothing. Interesting! "
    else:
        user_select += "'" + user_important + "'. "
    # for i in range(len(user_marks)):
    #     if user_marks[i][1] != None and h1[i][0] not in ["P", "N"]:
    #         flag_select = True
    #         user_select += "'" + h1[i][0] + "'"
    #         if i == len(h1) - 1:
    #             user_select += ". "
    #         else:
    #             user_select += ", "
    # if not flag_select:
    #     user_select += "nothing. Interesting! "
    user_select += "Wanna see how the AI made the guess? Click here. ⬅️"
    if lang_selected in ['en']:
        # 0 1 2 3 4 5 6 7 8 9 10
        if ai_predict == golden_label:
            if abs(human_predict - golden_label) <= 2: # Both correct
                golden_label = int((human_predict + ai_predict) / 2)
                ai_predict = golden_label
                chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! 🎉 Both of you get the correct answer!", user_select))
                num1 += 1
                num2 += 1
            else:
                golden_label += random.randint(-1, 1) * 0.5
                while golden_label > 10 or golden_label < 0:
                    golden_label += random.randint(-1, 1) * 0.5
                golden_label = int(golden_label)
                ai_predict = golden_label
                chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
                num2 += 1
        else:
            if abs(human_predict - golden_label) < abs(ai_predict - golden_label):
                if abs(human_predict - golden_label) < 2:
                    golden_label = int((golden_label + human_predict) / 2)
                    ai_predict += random.randint(-1, 1) * 0.5
                    ai_predict = int(ai_predict)
                    chatbot.append(("The correct answer is " + str(golden_label) + ". Great! 🎉 You are closer to the answer and better than AI!", user_select))
                    num1 += 1
                else:
                    golden_label = int(golden_label)
                    ai_predict = int(ai_predict)
                    chatbot.append(("The correct answer is " + str(golden_label) + ". Both wrong... Maybe next time you'll win!", user_select))
            else:
                golden_label = int(golden_label)
                ai_predict = int(ai_predict)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. No one gets the correct answer. But nice try! 😉", user_select))
    else:
        if golden_label == 10:
            if ai_predict > 5 and human_predict > 5:
                golden_label = int((human_predict + ai_predict)/2) + random.randint(-1, 1)
                while golden_label > 10:
                    golden_label = int((human_predict + ai_predict)/2) + random.randint(-1, 1)
                ai_predict = int((golden_label + ai_predict) / 2)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! 🎉 Both of you get the correct answer!", user_select))
                num1 += 1
                num2 += 1
            elif ai_predict > 5 and human_predict <= 5:
                golden_label -= random.randint(0, 3)
                ai_predict = 7 + random.randint(-1, 2)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
                num2 += 1
            elif ai_predict <= 5 and human_predict > 5:
                golden_label = human_predict + random.randint(-1, 1)
                while golden_label > 10:
                    golden_label = human_predict + random.randint(-1, 1)
                ai_predict = int(ai_predict)
                golden_label = int(golden_label)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Great! 🎉 You are close to the answer and better than AI!", user_select))
                num1 += 1
            else:
                golden_label = int(golden_label)
                ai_predict = int(ai_predict)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry... No one gets the correct answer. But nice try! 😉", user_select))
        else:
            if ai_predict < 5 and human_predict < 5:
                golden_label = int((human_predict + ai_predict)/2) + random.randint(-1, 1)
                while golden_label < 0:
                    golden_label = int((human_predict + ai_predict)/2) + random.randint(-1, 1)
                ai_predict = int((golden_label + ai_predict) / 2)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! 🎉 Both of you get the correct answer!", user_select))
                num1 += 1
                num2 += 1
            elif ai_predict < 5 and human_predict >= 5:
                golden_label += random.randint(0, 3)
                ai_predict = 3 + random.randint(-2, 1)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
                num2 += 1
            elif ai_predict >= 5 and human_predict < 5:
                golden_label = human_predict + random.randint(-1, 1)
                while golden_label < 0:
                    golden_label = human_predict + random.randint(-1, 1)
                ai_predict = int(ai_predict)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Great! 🎉 You are close to the answer and better than AI!", user_select))
                num1 += 1
            else:
                golden_label = int(golden_label)
                ai_predict = int(ai_predict)
                chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry... No one gets the correct answer. But nice try! 😉", user_select))

    # data = pd.DataFrame(
    #     {
    #         "Role": ["AI 🤖", "HUMAN 👨👩"],
    #         "Scores": [num2, num1],
    #     }
    # )
    # scroe_human = ''' # Human: ''' + str(int(num1))
    # scroe_robot = ''' # Robot: ''' + str(int(num2))
    # tot_scores = ''' ### <p style="text-align: center;"> 🤖 Machine &ensp; ''' + str(int(num2)) + ''' &ensp; VS &ensp; ''' + str(int(num1)) + ''' &ensp; Human 👨👩 </p>'''

    # tot_scores = ''' #### <p style="text-align: center;"> Today's Scores:</p>
    #                 #### <p style="text-align: center;"> 🤖 Machine &ensp; <span style="color: red;">''' + str(int(num2)) + '''</span> &ensp; VS &ensp; <span style="color: red;">''' + str(int(num1)) + '''</span> &ensp; Human 🙋 </p>'''
    tot_scores = ''' #### <p style="text-align: center;"> Today's Scores: &ensp; &ensp; 🤖 Machine &ensp; <span style="color: red;">''' + str(int(num2)) + '''</span> &ensp; VS &ensp; <span style="color: red;">''' + str(int(num1)) + '''</span> &ensp; Human 🙋 </p>'''
    
    # num_tmp = max(num1, num2)
    # y_lim_upper = (int((num_tmp + 3)/10)+1) * 10
    # figure = gr.BarPlot.update(
    #         data,
    #         x="Role",
    #         y="Scores",
    #         color="Role",
    #         vertical=False,
    #         y_lim=[0,y_lim_upper],
    #         color_legend_position='none', 
    #         height=250, 
    #         width=500,
    #         show_label=False,
    #         container=False,
    #     )
    # tooltip=["Role", "Scores"],
    return ai_predict, chatbot, num1, num2, tot_scores

def interpre1(lang_selected, num_selected):
    if lang_selected in ['en']:
        fname = 'data1_en.txt'
        tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
    else:
        fname = 'data1_nl_10.txt'
        tokenizer = AutoTokenizer.from_pretrained("DTAI-KULeuven/robbert-v2-dutch-sentiment")
        
    with open(fname) as f:
        content = f.readlines()
        text = eval(content[int(num_selected*2)])
        interpretation = eval(content[int(num_selected*2+1)])

    encodings = tokenizer(text['text'], return_offsets_mapping=True)

    print(encodings['offset_mapping'])
    is_subword = [False, False]
    for i in range(2, len(encodings['offset_mapping'])):
        if encodings['offset_mapping'][i][0] == encodings['offset_mapping'][i-1][1]:
            is_subword.append(True)
        else:
            is_subword.append(False)
    print(is_subword)
    interpretation_combined = []

    index_tmp = 0
    while index_tmp < (len(interpretation) - 1):
        if not is_subword[index_tmp+1]:
            interpretation_combined.append(interpretation[index_tmp])
            index_tmp += 1
        else:
            text_combined = interpretation[index_tmp][0]
            score_combinded = interpretation[index_tmp][1]
            length = 1
            while is_subword[index_tmp+length]:
                text_combined += interpretation[index_tmp+length][0]
                score_combinded += interpretation[index_tmp+length][1]
                length += 1
            interpretation_combined.append((text_combined, score_combinded/length))
            index_tmp += length

    interpretation_combined.append(('', 0.0))
    print(interpretation_combined)
    res = {"original": text['text'], "interpretation": interpretation_combined}
    # pos = []
    # neg = []
    # res = []
    # for i in interpretation:
    #     if i[1] > 0:
    #         pos.append(i[1])
    #     elif i[1] < 0:
    #         neg.append(i[1])
    #     else:
    #         continue
    # median_pos = np.median(pos)
    # median_neg = np.median(neg)


    # res.append(("P", "+"))
    # res.append(("/", None))
    # res.append(("N", "-"))
    # res.append(("Review:", None))
    # for i in interpretation:
    #     if i[1] > median_pos:
    #         res.append((i[0], "+"))
    #     elif i[1] < median_neg:
    #         res.append((i[0], "-"))
    #     else:
    #         res.append((i[0], None))
    return res
    
def func1_written(text_written, human_predict, lang_written):
    chatbot = []
    # num1: Human score; num2: AI score

    '''
    # (START) API version
    
    API_URL = "https://api-inference.huggingface.co/models/nlptown/bert-base-multilingual-uncased-sentiment"
    # API_URL = "https://api-inference.huggingface.co/models/cmarkea/distilcamembert-base-sentiment"
    headers = {"Authorization": "Bearer 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"}

    response = requests.post(API_URL, headers=headers, json=text_written)
    output = response.json()
    
    # result = dict()
    star2num = {
        "5 stars": 100,
        "4 stars": 75,
        "3 stars": 50,
        "2 stars": 25,
        "1 star": 0,
    }

    out = output[0][0]
    # (END) API version
    ''' 

    # (START) off-the-shelf version


    # tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
    # model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    classifier = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment", device=device)
    
    output = classifier([text_written])

    star2num = {
        "5 stars": 10,
        "4 stars": 7.5,
        "3 stars": 5,
        "2 stars": 2.5,
        "1 star": 0,
    }
    print(output)
    out = output[0]
    # (END) off-the-shelf version

    
    ai_predict = star2num[out['label']]
    # result[label] = out['score']

    if abs(ai_predict - human_predict) <= 2:
        ai_predict = int(ai_predict)
        chatbot.append(("AI gives it a close score! 🎉", "⬅️ Feel free to try another one! This time let’s see if you can trick the AI into giving a wrong rating. ⬅️"))
    else:
        ai_predict += int(random.randint(-1, 1))
        while ai_predict > 10 or ai_predict < 0:
            ai_predict += int(random.randint(-1, 1))
        chatbot.append(("AI thinks in a different way from human. 😉", "⬅️ Feel free to try another one! ⬅️"))

    # sentiment_classifier = pipeline("text-classification", return_all_scores=True)
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

    if lang_written == "Dutch":
        sentiment_classifier = pipeline("text-classification", model='DTAI-KULeuven/robbert-v2-dutch-sentiment', return_all_scores=True, device=device)
        tokenizer = AutoTokenizer.from_pretrained("DTAI-KULeuven/robbert-v2-dutch-sentiment")
    else:
        sentiment_classifier = pipeline("text-classification", model='distilbert-base-uncased-finetuned-sst-2-english', return_all_scores=True, device=device)
        tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

    explainer = shap.Explainer(sentiment_classifier)

    shap_values = explainer([text_written])
    interpretation = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))

    
    encodings = tokenizer(text_written, return_offsets_mapping=True)

    print(encodings['offset_mapping'])
    is_subword = [False, False]
    for i in range(2, len(encodings['offset_mapping'])):
        if encodings['offset_mapping'][i][0] == encodings['offset_mapping'][i-1][1]:
            is_subword.append(True)
        else:
            is_subword.append(False)
    print(is_subword)
    interpretation_combined = []

    index_tmp = 0
    while index_tmp < (len(interpretation) - 1):
        if not is_subword[index_tmp+1]:
            interpretation_combined.append(interpretation[index_tmp])
            index_tmp += 1
        else:
            text_combined = interpretation[index_tmp][0]
            score_combinded = interpretation[index_tmp][1]
            length = 1
            while is_subword[index_tmp+length]:
                text_combined += interpretation[index_tmp+length][0]
                score_combinded += interpretation[index_tmp+length][1]
                length += 1
            interpretation_combined.append((text_combined, score_combinded/length))
            index_tmp += length

    interpretation_combined.append(('', 0.0))
    print(interpretation_combined)

    res = {"original": text_written, "interpretation": interpretation_combined}
    print(res)

    return res, ai_predict, chatbot