import json

from util.assistants import GPTAgent
import json_repair

class evaluator:
    def __init__(self, model_name='GPT4-turbo'):
        self.model = GPTAgent(model_name)

    def validate_scores(self, scores):
        required_keys = ["Factually Correct", "Useful", "Context Specific", "User Specific", "Provides Pluralism"]
        for key in required_keys:
            if key not in scores or not isinstance(scores[key], (int, float)) or not (-1 <= scores[key] <= 1):
                return {"Factually Correct": -1,"Useful": -1,"Context Specific": -1,"User Specific":-1,"Provides Pluralism":-1}

        return scores

    def evaluate_single(self, question,explanation):

        evaluation_prompt = f"""You are provided with a user's question and the corresponding explanation generated by 
        an AI model. Your task is to evaluate the explanation based on the following five principles. Each principle 
        should be scored on a scale from 0 to 1, where 0 indicates that the principle is not met at all, 
        and 1 indicates that the principle is fully satisfied.
        
        Question: 
        {question}
        
        Provided Explanation: 
        {explanation}
        
        Evaluation Criteria:

        Factually Correct:
        Definition: The explanation must be accurate and relevant to the question and the subject matter.
        Score: (0-1) How factually correct is the explanation? Consider the accuracy of the details provided and their relevance to the question.
        
        Useful:
        Definition: The explanation should enable the user to understand the answer better and should facilitate further reasoning or decision-making.
        Score: (0-1) How useful is the explanation in helping the user understand the answer and make informed decisions?
        
        Context Specific:
        Definition: The explanation should be relevant to the specific context or scenario implied by the question.
        Score: (0-1) How well does the explanation address the specific context or scenario of the question?
        
        User Specific:
        Definition: The explanation should cater to the knowledge level and interests of the user, assuming typical or specified user characteristics.
        Score: (0-1) How well does the explanation cater to the needs and knowledge level of the intended user?
        
        Provides Pluralism:
        Definition: The explanation should offer or accommodate multiple viewpoints or interpretations, allowing the user to explore various perspectives.
        Score: (0-1) How well does the explanation provide or support multiple perspectives?
    
        After evaluating the provided question and explanation based on the five principles, please format your scores in a JSON dictionary. Directly provide me with the json without any additional text.
 
        Example JSON format:
        
        Answer:{{"Factually Correct": 0.9,"Useful": 0.85,"Context Specific": 0.8,"User Specific": 0.75,"Provides Pluralism": 0.7}}
        
        Answer:
        """

        response = self.model.invoke(evaluation_prompt,temperature=0, max_tokens=500).strip()
        #response = """{{"Factually Correct": 0.9,"Useful": 0.85,"Context Specific": 0.8,"User Specific": 0.75,"Provides Pluralism": 0.7}}"""
        print(response)
        try:
            scores = json.loads(response)
        except json.JSONDecodeError:
            # Attempt to repair the JSON if decoding fails
            repaired_json = json_repair.repair_json(response, skip_json_loads=True, return_objects=False)
            try:
                scores = json.loads(repaired_json)
            except json.JSONDecodeError:
                print("Failed to decode JSON response even after repair attempt. Skipping this batch.")
                return {"Factually Correct": -1,"Useful": -1,"Context Specific": -1,"User Specific":-1,"Provides Pluralism":-1}


        return self.validate_scores(scores)

    def format_conversation(self, conversation):
        formatted_conversation = "\n".join(
            f"{exchange['role'].capitalize()}: {exchange['content']}" for exchange in conversation
        )
        return formatted_conversation

    def evaluate_conversation(self, conversation, context):
        formatted_conversation = self.format_conversation(conversation)
        evaluation_prompt = f"""
        You are provided with a conversation between a user and a chatbot and the context about them. Your task is to evaluate the chatbot explanation in the conversation based on the following five principles. Each principle should be scored on a scale from 0 to 1, where 0 indicates that the principle is not met at all, and 1 indicates that the principle is fully satisfied.

        Conversation:
        {formatted_conversation}

        Context:
        {context}

        Evaluation Criteria:

        Factually Correct:
        Definition: The explanation must be accurate and relevant to the question and the subject matter.
        Score: (0-1) How factually correct is the explanation? Consider the accuracy of the details provided and their relevance to the question.

        Useful:
        Definition: The explanation should enable the user to understand the answer better and should facilitate further reasoning or decision-making.
        Score: (0-1) How useful is the explanation in helping the user understand the answer and make informed decisions?

        Context Specific:
        Definition: The explanation should be relevant to the specific context or scenario implied by the question.
        Score: (0-1) How well does the explanation address the specific context or scenario of the question?

        User Specific:
        Definition: The explanation should cater to the knowledge level and interests of the user, assuming typical or specified user characteristics.
        Score: (0-1) How well does the explanation cater to the needs and knowledge level of the intended user?

        Provides Pluralism:
        Definition: The explanation should offer or accommodate multiple viewpoints or interpretations, allowing the user to explore various perspectives.
        Score: (0-1) How well does the explanation provide or support multiple perspectives?

        After evaluating the provided conversation based on the context and five principles, please format your scores in a JSON dictionary. Directly provide me with the json without any additional text.

        Example JSON format:

        Answer: {{"Factually Correct": 0.9, "Useful": 0.85, "Context Specific": 0.8, "User Specific": 0.75, "Provides Pluralism": 0.7}}

        Answer:
        """

        print(evaluation_prompt)

        response = self.model.invoke(evaluation_prompt, temperature=0, max_tokens=500).strip()
        try:
            scores = json.loads(response)
        except json.JSONDecodeError:
            repaired_json = json_repair.repair_json(response, skip_json_loads=True, return_objects=False)
            try:
                scores = json.loads(repaired_json)
            except json.JSONDecodeError:
                print("Failed to decode JSON response even after repair attempt. Skipping this batch.")
                return {key: -1 for key in ["Factually Correct", "Useful", "Context Specific", "User Specific", "Provides Pluralism"]}

        return self.validate_scores(scores)

def write_evaluation_commentary(scores):
    evaluation_details = []
    for principle, score in scores.items():

        if score == -1:
            evaluation_details.append({'Principle': principle, 'Score': score, 'Commentary': 'Failed to evaluate the explanation.'})
            continue

        if principle == "Factually Correct":
            if score >= 0.8:
                comment = "Excellent accuracy! The information is precise and directly relevant to the question."
            elif score >= 0.5:
                comment = "Moderately accurate, but some details may not be completely correct or are somewhat irrelevant."
            else:
                comment = "The explanation contains significant inaccuracies or irrelevant information."
        elif principle == "Useful":
            if score >= 0.8:
                comment = "Highly useful! The explanation clearly enhances understanding and aids in further reasoning or decision-making."
            elif score >= 0.5:
                comment = "Somewhat useful, though it could be more insightful or practical in aiding understanding."
            else:
                comment = "The explanation does little to help understand or apply the information provided."
        elif principle == "Context Specific":
            if score >= 0.8:
                comment = "Perfectly tailored to the context of the question, addressing the specific scenario effectively."
            elif score >= 0.5:
                comment = "Generally addresses the context, but may miss specific details or nuances relevant to the question."
            else:
                comment = "Fails to address the context of the question, lacking relevance or specificity."
        elif principle == "User Specific":
            if score >= 0.8:
                comment = "The explanation is well-adapted to the user's knowledge level and interests, demonstrating thoughtfulness."
            elif score >= 0.5:
                comment = "Moderately considerate of the user's knowledge level, but could be more tailored."
            else:
                comment = "Does not consider the user's background or interests, potentially leading to confusion or disinterest."
        elif principle == "Provides Pluralism":
            if score >= 0.8:
                comment = "Provides an excellent range of perspectives or interpretations, fostering a comprehensive understanding."
            elif score >= 0.5:
                comment = "Offers some alternative perspectives, but more could be provided to enrich understanding."
            else:
                comment = "Lacks diversity in viewpoints, limiting the depth of exploration into the topic."

        evaluation_details.append({'Principle': principle, 'Score': score, 'Commentary': comment})
    return evaluation_details

if __name__ == '__main__':

    eval = evaluator()
    conversation = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who won the world series in 2020?"},
        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
        {"role": "user", "content": "Where was it played?"}
    ]
    context = "general user, user_background is sports enthusiast"
    results = eval.evaluate_conversation(conversation, context)
    print(results)
    print(write_evaluation_commentary(results))