import pandas as pd
from fuzzywuzzy import fuzz
from collections import Counter
from nltk.stem import PorterStemmer
from ast import literal_eval
from typing import Union, List

class KBVQAEvaluator:
    def __init__(self):
        """
        Initialize the VQA Processor with the dataset and configuration settings.
        """
        self.use_fuzzy = False
        self.stemmer = PorterStemmer()
        self.df = pd.read_excel('evaluation_results_final.xlsx')
        self.scores_df = pd.read_excel(data_path, sheet_name="Scores")
        self.df = pd.read_excel(data_path, sheet_name="Main Data")
        self.vqa_scores = {}
        self.exact_match_scores = {}

    def stem_answers(self, answers: Union[str, List[str]]) -> Union[str, List[str]]:
        """
        Apply Porter Stemmer to either a single string or a list of strings.
        """
        if isinstance(answers, list):
            return [" ".join(self.stemmer.stem(word.strip()) for word in answer.split()) for answer in answers]
        else:
            words = answers.split()
            return " ".join(self.stemmer.stem(word.strip()) for word in words)

    def calculate_vqa_score(self, ground_truths, model_answer):
        """
        Calculate VQA score based on the number of matching answers, with optional fuzzy matching.
        """
        if self.use_fuzzy:
            fuzzy_matches = sum(fuzz.partial_ratio(model_answer, gt) >= 80 for gt in ground_truths)
            return min(fuzzy_matches / 3, 1)
        else:
            count = Counter(ground_truths)
            return min(count.get(model_answer, 0) / 3, 1)

    def calculate_exact_match_score(self, ground_truths, model_answer):
        """
        Calculate Exact Match score, with optional fuzzy matching.
        """
        if self.use_fuzzy:
            return int(any(fuzz.partial_ratio(model_answer, gt) >= 80 for gt in ground_truths))
        else:
            return int(model_answer in ground_truths)

    def evaluate(self):
        """
        Process the DataFrame: stem answers, calculate scores, and store results.
        """
        self.df['raw_answers_stemmed'] = self.df['raw_answers'].apply(literal_eval).apply(self.stem_answers)
        model_configurations = ['caption+detic', 'caption+yolov5', 'only_caption', 'only_detic', 'only_yolov5']
        model_names = ['13b', '7b']
        
        for name in model_names:
            for config in model_configurations:
                full_config = f'{name}_{config}'
                self.df[f'{full_config}_stemmed'] = self.df[full_config].apply(self.stem_answers)
                
                self.df[f'vqa_score_{full_config}'] = self.df.apply(lambda x: self.calculate_vqa_score(x['raw_answers_stemmed'], x[f'{full_config}_stemmed']), axis=1)
                self.df[f'exact_match_score_{full_config}'] = self.df.apply(lambda x: self.calculate_exact_match_score(x['raw_answers_stemmed'], x[f'{full_config}_stemmed']), axis=1)
                
                self.vqa_scores[full_config] = round(self.df[f'vqa_score_{full_config}'].mean()*100, 2)
                self.exact_match_scores[full_config] = round(self.df[f'exact_match_score_{full_config}'].mean()*100, 2)
                
    def save_results(self):
        # Create a DataFrame for the scores
        scores_data = {
            'Model Configuration': list(self.vqa_scores.keys()),
            'VQA Score': list(self.vqa_scores.values()),
            'Exact Match Score': list(self.exact_match_scores.values())
        }
        scores_df = pd.DataFrame(scores_data)

        # Saving the scores DataFrame to an Excel file
        with pd.ExcelWriter('evaluation_results_final.xlsx', engine='openpyxl', mode='w') as writer:
            scores_df.to_excel(writer, sheet_name='Scores', index=False)

    def run_evaluator(self):
        #evaluator.evaluate()
        st.table(self.scores_df)
        st.write(self.scores_df)
        #print(evaluator.exact_match_scores)
        #evaluator.save_results()