my_model/results/evaluation.py

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
import streamlit as st
from my_model.config import evaluation_config as config 

class KBVQAEvaluator:
    def __init__(self):
        """
        Initialize the VQA Processor with the dataset and configuration settings.
        """
        self.data_path = config.EVALUATION_DATA_PATH
        self.use_fuzzy = config.USE_FUZZY
        self.stemmer = PorterStemmer()
        self.scores_df = pd.read_excel(self.data_path, sheet_name="Scores")
        self.df = pd.read_excel(self.data_path, sheet_name="Main Data")
        self.vqa_scores = {}
        self.exact_match_scores = {}
        self.fuzzy_threshold = config.FUZZY_SCORE
        self.openai_api_key = config.OPENAI_API_KEY
        self.model_names = config.MODEL_NAMES
        self.model_configurations = config.MODEL_CONFIGURATIONS  # ['caption+detic', 'caption+yolov5', 'only_caption', 'only_detic', 'only_yolov5']
        self.gpt4_seed = config.GPT4_SEED
        self.gpt4_max_tokens = config.GPT4_MAX_TOKENS
        self.gpt4_temperature = config.GPT4_TEMPERATURE

    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) >= self.fuzzy_threshold 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) >= self.fuzzy_threshold for gt in ground_truths))
        else:
            return int(model_answer in ground_truths)

    def syntactic_evaluation(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)
        
        for name in self.model_names:
            for config in self.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 create_GPT4_messages_template(self, question, ground_truths, model_answer):
        """
        Create a message list for the GPT-4 API call based on the question, ground truths, and model answer.
        """
        system_message = {
            "role": "system",
            "content": """You are an AI trained to evaluate the equivalence of AI-generated answers to a set of ground truth answers for a given question. Upon reviewing a model's answer, determine if it matches the ground truths. Use the following rating system: 1 if you find that the model answer matches more than 25% of the ground truth answers, 2 if you find that the model answer matches only less than 25% of the ground truth answers, and 3 if the model answer is incorrect. Respond in the format below for easy parsing:
                        Rating: {1/2/3}
                       """
                     }
    
        user_message = {
            "role": "user", 
            "content": f"Question : {question}\nGround Truth: {ground_truths}\nModel's Response: {model_answer}"
        }

        return [system_message, user_message]
        

    def semantic_evaluation(self):
        """
        Perform semantic evaluation using GPT-4 for each model configuration.
        """
        openai.api_key = self.openai_api_key
        model_configurations_for_semantic_evaluation = self.model_configurations[:2] # considering only main model configs ['caption+detic', 'caption+yolov5'] without ablation, due to the cost involved.
        for name in self.model_names:
            for config in model_configurations_for_semantic_evaluation:
                # Iterate over rows and send requests
                for index, row in self.df.iterrows():
                    messages = self.create_GPT4_messages_template(row['question'], row['raw_answers'][1:-1], row[name+'_'+config])
                    response = openai.ChatCompletion.create(model="gpt-4", messages=messages, max_tokens=self.gpt4_max_tokens, temperature=self.gpt4_temperature, seed=self.gpt4_seed)
                    evaluation = response["choices"][0]["message"]["content"]
                    rating = int(evaluation.split('\n')[0].split(":")[1].strip())
                    self.df.at[index, f'gpt4_rating_{config}'] = rating    
                
    def save_results(self, save_filename):
        # 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(save_filename+'.xlsx', engine='openpyxl', mode='w') as writer:
            self.df.to_excel(writer, sheet_name='Main Data', index=False)
            scores_df.to_excel(writer, sheet_name='Scores', index=False)

def run_evaluation(save=False, save_filename="results"):
    """
    Run the full evaluation process using KBVQAEvaluator and save the results to an Excel file.
    """
    # Instantiate the evaluator
    evaluator = KBVQAEvaluator()
    
    # Run syntactic evaluation
    evaluator.syntactic_evaluation()
    
    # Optionally, run semantic evaluation if required (can be cost-intensive)
    evaluator.semantic_evaluation()
    
    if save:
        # Save results
        evaluator.save_results(save_filename)

# Call run_evaluation() to execute the evaluation process
if __name__ == "__main__":
    #run_evaluation(save=True, save_filename="results")
    pass