from prompt import Prompt
from openai import OpenAI
from fuzzywuzzy import fuzz
from fuzzywuzzy import process

import gradio as gr
import pandas as pd
import os,json
import time

QUESTION_DICT = {
    "Question 1": "Animal Type",
    "Question 2": "Exposure Age",
    "Question 3": "Behavior Test",
    "intervention_1": "Intervention 1",
    "intervention_2": "Intervention 2",
    "Question 5": "Genetic Chain",
    "Question 6": "Issues or Challenge Resolved",
    "Question 7": "Innovations in Methodology",
    "Question 8": "Impact of Findings",
    "Question 9": "limitations",
    "Question 10": "Potential Applications",
}

REVERSE_QUESTION_DICT = {
    "Animal Type": "Question 1",
    "Exposure Age": "Question 2",
    "Behavior Test": "Question 3",
    "Intervention 1": "Question 4",
    "Intervention 2": "Question 5",
    "Genetic Chain": "Question 6",
    "Issues or Challenge Resolved": "Question 7",
    "Innovations in Methodology": "Question 8",
    "Impact of Findings": "Question 9",
    "limitations": "Question 10",
    "Potential Applications": "Question 11",
}

class Backend:
    def __init__(self):
        self.agent = OpenAI()
        self.prompt = Prompt()

    def read_file_single(self, file):
        # read the file
        if file is not None:
            with open(file.name, 'r') as f:
                text = f.read()
        else:
            raise gr.Error("You need to upload a file first")
        return text
    
    def phrase_pdf(self, file_path):
        from langchain.document_loaders import UnstructuredPDFLoader
        loader = UnstructuredPDFLoader(file_path, model = 'elements')
        file = loader.load()
        return file[0].page_content

    def read_file(self, files):
        # read the file
        text_list = []
        self.filename_list = []
        if files is not None:
            for file in files:
                if file.name.split('.')[-1] == 'pdf':
                    # convert pdf to txt
                    text = self.phrase_pdf(file.name)
                    
                else:
                    with open(file.name, 'r', encoding='utf-8') as f:
                        text = f.read()

                text_list.append(text)
                self.filename_list.append(file.name.split('\\')[-1])
        else:
            raise gr.Error("You need to upload a file first")
        return text_list
    
    def highlight_text(self, text, highlight_list):
        # Find the original sentences
        # Split the passage into sentences
        # sentences_in_passage = text.replace('\n', '')
        sentences_in_passage = text.split('.')
        sentences_in_passage = [i.split('\n') for i in sentences_in_passage]
        new_sentences_in_passage = []
        for i in sentences_in_passage:
            new_sentences_in_passage = new_sentences_in_passage + i
        new_sentences_in_passage = [i for i in new_sentences_in_passage if len(i) > 10]

        # hightlight the reference
        for hl in highlight_list:
            # Find the best match using fuzzy matching
            best_match = process.extractOne(hl, new_sentences_in_passage, scorer=fuzz.partial_ratio)
            text = text.replace(best_match[0], f'<mark style="background: #A5D2F1">{best_match[0]}</mark><mark style="background: #FFC0CB"><font color="red"> (match score:{best_match[1]})</font></mark>')

        # add line break
        text = text.replace('\n', f" <br /> ")

        # add scroll bar
        text = f'<div style="height: 600px; overflow: auto;">{text}</div>'

        return text
    
    def process_file_online(self, file, questions, openai_key, model_selection, progress = gr.Progress()):
        # record the questions
        self.questions = questions

        # get the text_list
        self.text_list = self.read_file(file)

        # make the prompt
        prompt_list = [self.prompt.get(text, questions, 'v3') for text in self.text_list]

        # select the model
        if model_selection == 'ChatGPT':
            model = 'gpt-3.5-turbo-16k'
        elif model_selection == 'GPT4':
            model = 'gpt-4-1106-preview'

        # interact with openai
        self.res_list = []
        for prompt in progress.tqdm(prompt_list, desc = 'Generating answers...'):
            res = self.agent(prompt, with_history = False, temperature = 0.1, model = model, api_key = openai_key)
            res = self.prompt.process_result(res, 'v3')
            self.res_list.append(res)

        # Use the first file as default
        # Use the first question for multiple questions
        gpt_res = self.res_list[0]
        self.gpt_result = gpt_res

        self.current_question = 0
        self.totel_question = len(res.keys())
        self.current_passage = 0
        self.total_passages = len(self.res_list)

        # make a dataframe to record everything
        self.ori_answer_df = pd.DataFrame()
        self.answer_df = pd.DataFrame()
        for i, res in enumerate(self.res_list):
            tmp = pd.DataFrame(res).T
            tmp = tmp.reset_index()
            tmp = tmp.rename(columns={"index":"question_id"})
            tmp['filename'] = self.filename_list[i]
            tmp['question'] = self.questions
            self.ori_answer_df = pd.concat([tmp, self.ori_answer_df])
            self.answer_df = pd.concat([tmp, self.answer_df])

        # default fist question
        res = res['Question 1']
        question = self.questions[self.current_question]
        self.answer = res['answer']
        self.text = self.text_list[0]
        self.highlighted_out = res['original sentences']
        highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
        self.highlighted_out = '\n'.join(self.highlighted_out)

        file_name = self.filename_list[self.current_passage]
        
        return file_name, question, self.answer, self.highlighted_out, highlighted_out_html, self.answer, self.highlighted_out
    
    def process_results(self, answer_correct, correct_answer, reference_correct, correct_reference):
        if not hasattr(self, 'clicked_correct_answer'):
            raise gr.Error("You need to judge whether the generated answer is correct first")

        if not hasattr(self, 'clicked_correct_reference'):
            raise gr.Error("You need to judge whether the highlighted reference is correct first")

        if not hasattr(self, 'answer_df'):
            raise gr.Error("You need to submit the document first")
        
        if self.current_question >= self.totel_question or self.current_question < 0:
            raise gr.Error("No more questions, please return back")
                
        # record the answer
        condition = (self.answer_df['question_id'] == f'Question {self.current_question + 1}' ) & \
            (self.answer_df['filename'] == self.filename_list[self.current_passage]) 
        self.answer_df.loc[condition, 'answer_correct'] = answer_correct
        self.answer_df.loc[condition, 'reference_correct'] = reference_correct

        # self.answer_df.loc[f'Question {self.current_question + 1}', 'answer_correct'] = answer_correct
        # self.answer_df.loc[f'Question {self.current_question + 1}', 'reference_correct'] = reference_correct
        
        if self.clicked_correct_answer == True:
            if hasattr(self, 'answer'):
                self.answer_df.loc[condition, 'correct_answer'] = self.answer
            else:
                raise gr.Error("You need to submit the document first")
        else:
            # self.answer_df.loc[f'Question {self.current_question + 1}', 'correct_answer'] = correct_answer
            self.answer_df.loc[condition, 'correct_answer'] = correct_answer
        
        if self.clicked_correct_reference == True:
            if hasattr(self, 'highlighted_out'):
                self.answer_df.loc[condition, 'correct_reference'] = self.highlighted_out
            else:
                raise gr.Error("You need to submit the document first")
        else:
            self.answer_df.loc[condition, 'correct_reference'] = correct_reference
        
        gr.Info('Results saved!')
        return "Results saved!"
    
    def process_next(self):
        self.current_question += 1
        if hasattr(self, 'clicked_correct_answer'):
            del self.clicked_correct_answer
        if hasattr(self, 'clicked_correct_reference'):
            del self.clicked_correct_reference

        if self.current_question >= self.totel_question:
            # self.current_question -= 1
            return "No more questions!", "No more questions!", "No more questions!", "No more questions!", "No more questions!", 'No more questions!', 'No more questions!', 'Still need to click the button above to save the results', None, None
        else:
            # res = self.gpt_result[f'Question {self.current_question + 1}']
            res = self.gpt_result[list(self.gpt_result.keys())[self.current_question]]
            question = self.questions[self.current_question]
            self.answer = res['answer']
            self.highlighted_out = res['original sentences']
            highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
            self.highlighted_out = '\n'.join(self.highlighted_out)
            file_name = self.filename_list[self.current_passage]

            return file_name, question, self.answer, self.highlighted_out, highlighted_out_html, 'Please judge on the generated answer', 'Please judge on the generated answer', 'Still need to click the button above to save the results', None, None

    def process_last(self):
        self.current_question -= 1

        # To make sure to correct the answer first
        if hasattr(self, 'clicked_correct_answer'):
            del self.clicked_correct_answer
        if hasattr(self, 'clicked_correct_reference'):
            del self.clicked_correct_reference
        
        # check question boundary
        if self.current_question < 0:
            # self.current_question += 1
            return "No more questions!", "No more questions!", "No more questions!", "No more questions!", "No more questions!", 'No more questions!', 'No more questions!', 'Still need to click the button above to save the results', None, None
        else:
            # res = self.gpt_result[f'Question {self.current_question + 1}']
            res = self.gpt_result[list(self.gpt_result.keys())[self.current_question]]
            question = self.questions[self.current_question]
            self.answer = res['answer']
            self.highlighted_out = res['original sentences']
            highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
            self.highlighted_out = '\n'.join(self.highlighted_out)
            file_name = self.filename_list[self.current_passage]
            return file_name, question, self.answer, self.highlighted_out, highlighted_out_html, 'Please judge on the generated answer', 'Please judge on the generated answer', 'Still need to click the button above to save the results', None, None

    def switch_next_passage(self):
        self.current_question = 0

        # To make sure to correct the answer first
        if hasattr(self, 'clicked_correct_answer'):
            del self.clicked_correct_answer
        if hasattr(self, 'clicked_correct_reference'):
            del self.clicked_correct_reference

        self.current_passage += 1
        

        if self.current_passage >= self.total_passages:
            # self.current_passage -= 1
            return "No more passages!", "No more passages!", "No more passages!", "No more passages!", "No more passages!", 'No more passages!', 'No more passages!', 'Still need to click the button above to save the results', None, None
        else:
            self.text = self.text_list[self.current_passage]
            gpt_res = self.res_list[self.current_passage]
            self.gpt_result = gpt_res
            # res = self.gpt_result[f'Question {self.current_question + 1}']
            res = self.gpt_result[list(self.gpt_result.keys())[self.current_question]]
            question = self.questions[self.current_question]
            self.answer = res['answer']
            self.highlighted_out = res['original sentences']
            highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
            self.highlighted_out = '\n'.join(self.highlighted_out)
            file_name = self.filename_list[self.current_passage]
            return file_name, question, self.answer, self.highlighted_out, highlighted_out_html, 'Please judge on the generated answer', 'Please judge on the generated answer', 'Still need to click the button above to save the results', None, None

    def switch_last_passage(self):
        self.current_question = 0

        # To make sure to correct the answer first
        if hasattr(self, 'clicked_correct_answer'):
            del self.clicked_correct_answer
        if hasattr(self, 'clicked_correct_reference'):
            del self.clicked_correct_reference

        self.current_passage -= 1

        if self.current_passage < 0:
            # self.current_passage += 1
            return "No more passages!", "No more passages!", "No more passages!", "No more passages!", "No more passages!", 'No more passages!', 'No more passages!', 'Still need to click the button above to save the results', None, None
        else:
            self.text = self.text_list[self.current_passage]
            gpt_res = self.res_list[self.current_passage]
            self.gpt_result = gpt_res
            # res = self.gpt_result[f'Question {self.current_question + 1}']
            res = self.gpt_result[list(self.gpt_result.keys())[self.current_question]]
            question = self.questions[self.current_question]
            self.answer = res['answer']
            self.highlighted_out = res['original sentences']
            highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
            self.highlighted_out = '\n'.join(self.highlighted_out)
            file_name = self.filename_list[self.current_passage]
            return file_name, question, self.answer, self.highlighted_out, highlighted_out_html, 'Please judge on the generated answer', 'Please judge on the generated answer', 'Still need to click the button above to save the results', None, None

    def download_answer(self, path = './tmp', name = 'answer.xlsx'):
        path = os.path.join(path,str(time.time()))
        os.makedirs(path, exist_ok = True)
        path = os.path.join(path, name)
        # self.ori_answer_df['questions'] = self.questions
        if not hasattr(self, 'ori_answer_df'):
            raise gr.Error("You need to submit the document first")
        else:
            self.ori_answer_df.to_excel(path, index = False)

        return path
    
    def download_corrected(self, path = './tmp', name = 'corrected_answer.xlsx'):
        path = os.path.join(path,str(time.time()))
        os.makedirs(path, exist_ok = True)
        path = os.path.join(path, name)
        # self.answer_df['questions'] = self.questions
        if not hasattr(self, 'answer_df'):
            raise gr.Error("You need to submit the document first")
        else:
            self.answer_df.to_excel(path, index = False)

        return path
    
    def change_correct_answer(self, correctness):
        if correctness == "Correct":
            self.clicked_correct_answer = True
            return "No need to change"
        else:
            if hasattr(self, 'answer'):
                self.clicked_correct_answer = False
                return self.answer
            else:
                return "No answer yet, you need to submit the document first"   
        
    def change_correct_reference(self, correctness):
        if correctness == "Correct":
            self.clicked_correct_reference = True
            return "No need to change"
        else:
            if hasattr(self, 'highlighted_out'):
                self.clicked_correct_reference = False
                return self.highlighted_out
            else:
                return "No answer yet, you need to submit the document first"

    def phase_df(self, df, questions):
        df = json.loads(df.T.to_json())
        res_list = []
        for key, item in df.items():
            tmp_res_list = {}

            if 'Question 1' in item and "Animal Type" in questions:
                tep_res_list_q1 = {
                    'answer': item['Question 1'],
                    'original sentences': eval(item['Question 1_original_sentences']),
                }
                tmp_res_list['Question 1'] = tep_res_list_q1

            if 'Question 2' in item and 'Exposure Age' in questions:
                tep_res_list_q2 = {
                    'answer': item['Question 2'],
                    'original sentences': eval(item['Question 2_original_sentences']),
                }
                tmp_res_list['Question 2'] = tep_res_list_q2

            if 'Question 3' in item and 'Behavior Test' in questions:
                tep_res_list_q3 = {
                    'answer': item['Question 3'],
                    'original sentences': eval(item['Question 3_original_sentences']),
                }
                tmp_res_list['Question 3'] = tep_res_list_q3
            
            if 'intervention_1' in item and "Intervention 1" in questions:
                tep_res_list_q4 = {
                    'answer': item['intervention_1'],
                    'original sentences': eval(item['Question 4intervention_1_original_sentences']),
                }
                tmp_res_list['Question 4'] = tep_res_list_q4
            
            if 'intervention_2' in item and "Intervention 2" in questions:
                tep_res_list_q5 = {
                    'answer': item['intervention_2'],
                    'original sentences': eval(item['Question 4intervention_2_original_sentences']),
                }
                tmp_res_list['Question 5'] = tep_res_list_q5
            
            if 'Question 5' in item and "Genetic Chain" in questions:
                tep_res_list_q6 = {
                    'answer': item['Question 5'],
                    'original sentences': eval(item['Question 5_original_sentences']),
                }
                tmp_res_list['Question 6'] = tep_res_list_q6
            
            if 'Question 6' in item and "Issues or Challenge Resolved" in questions:
                tep_res_list_q7 = {
                    'answer': item['Question 6'],
                    'original sentences': eval(item['Question 6_original_sentences']),
                }
                tmp_res_list['Question 7'] = tep_res_list_q7
            
            if 'Question 7' in item and "Innovations in Methodology" in questions:
                tep_res_list_q8 = {
                    'answer': item['Question 7'],
                    'original sentences': eval(item['Question 7_original_sentences']),
                }
                tmp_res_list['Question 8'] = tep_res_list_q8
            
            if 'Question 8' in item and "Impact of Findings" in questions:
                tep_res_list_q9 = {
                    'answer': item['Question 8'],
                    'original sentences': eval(item['Question 8_original_sentences']),
                }
                tmp_res_list['Question 9'] = tep_res_list_q9
            
            if 'Question 9' in item and "limitations" in questions:
                tep_res_list_q10 = {
                    'answer': item['Question 9'],
                    'original sentences': eval(item['Question 9_original_sentences']),
                }
                tmp_res_list['Question 10'] = tep_res_list_q10
            
            if 'Question 10' in item and "Potential Applications" in questions:
                tep_res_list_q11 = {
                    'answer': item['Question 10'],
                    'original sentences': eval(item['Question 10_original_sentences']),
                }
                tmp_res_list['Question 11'] = tep_res_list_q11
            
            res_list.append(tmp_res_list)

            # checking 
            for i in questions:
                if REVERSE_QUESTION_DICT[i] not in tmp_res_list:
                    raise gr.Error(f"Question {i} is not in the answer list, Please don't select it!")

        return res_list

    def process_file_offline(self, questions, answer_type, progress = gr.Progress()):
        # record the questions
        # self.questions = questions
        self.questions = [f"[ Question {i + 1}/{len(questions)} ] {q}" for i, q in enumerate(questions)]

        # get the text_list
        if answer_type == 'ChatGPT_txt':
            df = pd.read_csv('./offline_results/results_all.csv')
        elif answer_type == 'GPT4_txt':
            df = pd.read_csv('./offline_results/results_all_gpt4.csv')
        elif answer_type == 'New_GPT_4_pdf':
            df = pd.read_csv('./offline_results/results_new_pdf.csv')
        elif answer_type == 'Exp_training':
            df = pd.read_csv('./offline_results/exp_test.csv')
        elif answer_type == 'Exp_Group_A':
            df = pd.read_csv('./offline_results/exp_ga.csv')
        elif answer_type == 'Exp_Group_B':
            df = pd.read_csv('./offline_results/exp_gb.csv')

        # make the prompt
        self.res_list = self.phase_df(df, questions)

        if answer_type in ['ChatGPT_txt', 'GPT4_txt', 'New_GPT_4_pdf']:
            if answer_type == 'ChatGPT_txt' or answer_type == 'GPT4_txt':
                txt_root_path = './20230808-AI coding-1st round'
                self.filename_list = df['fn'].tolist()
            elif answer_type == 'New_GPT_4_pdf':
                txt_root_path = './new_pdfs'
                self.filename_list = df['fn'].tolist()
                self.filename_list = ['.'.join(f.split('.')[:-1]) + '.txt' for f in self.filename_list]

            self.text_list = []
            for file in progress.tqdm(self.filename_list):
                if file.split('.')[-1] == 'pdf':
                    # convert pdf to txt
                    text = self.phrase_pdf(os.path.join(txt_root_path, file))

                else:    
                    text_path = os.path.join(txt_root_path, file)
                    with open(text_path, 'r', encoding='utf-8') as f:
                        text = f.read()

                self.text_list.append(text)
            
        elif answer_type in ['Exp_training', 'Exp_Group_A', 'Exp_Group_B']:
            self.filename_list = df['fn'].tolist()
            if "Passage" not in self.filename_list[0]:
                self.filename_list = [f"[ Passage {i + 1}/{len(self.filename_list)} ] {self.filename_list[i]}" for i in range(len(self.filename_list))]
            self.text_list = df['content'].tolist()
        

        # Use the first file as default
        # Use the first question for multiple questions
        gpt_res = self.res_list[0]
        self.gpt_result = gpt_res

        self.current_question = 0
        self.totel_question = len(self.res_list[0].keys())
        self.current_passage = 0
        self.total_passages = len(self.res_list)

        # make a dataframe to record everything
        self.ori_answer_df = pd.DataFrame()
        self.answer_df = pd.DataFrame()
        for i, res in enumerate(self.res_list):
            tmp = pd.DataFrame(res).T
            tmp = tmp.reset_index()
            tmp = tmp.rename(columns={"index":"question_id"})
            tmp['filename'] = self.filename_list[i]
            tmp['question'] = self.questions
            self.ori_answer_df = pd.concat([tmp, self.ori_answer_df])
            self.answer_df = pd.concat([tmp, self.answer_df])

        # default fist question
        gpt_res = gpt_res[list(gpt_res.keys())[0]]
        question = self.questions[self.current_question]
        self.answer = gpt_res['answer']
        self.text = self.text_list[0]
        self.highlighted_out = gpt_res['original sentences']
        highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
        self.highlighted_out = '\n'.join(self.highlighted_out)

        file_name = self.filename_list[self.current_passage]
        
        return file_name, question, self.answer, self.highlighted_out, highlighted_out_html, self.answer, self.highlighted_out