# EvalifAI © tutorial

import gradio as gr
from datetime import datetime
from tempfile import TemporaryDirectory
import os
import json
from datasets import load_dataset
import dill

from huggingface_hub import hf_hub_download

from utils import list_rglob_files, load_json_file, create_directory

model_path = hf_hub_download(repo_id='flavienbwk/example-dummy-evaluation', filename='example-dummy-evaluation.dill')
with open(model_path, 'rb') as dill_file:
    model = dill.load(dill_file)

def get_occurences(sentence: str) -> dict:
    return model(sentence)

def evaluate() -> str:
    """
    Dummy evaluation example for EvalifAI © tutorial
    This program sends sentences to a model that must
    return the number of occurences of the letter "a".
    """

    with TemporaryDirectory() as tmpdirname:
        absolute_path = os.path.abspath(tmpdirname)
        create_directory(absolute_path)

        # Step 1 : initializing benchmark results
        report = {
            "started_at": None,
            "ended_at": None,
            "score": 0.0,
            "success": 0,
            "failure": 0,
            "metrics": {"valid_responses": 0.0, "success_rate": 0.0},
        }

        # Step 2 : checking input data format and gathering sentences
        dataset = load_dataset("flavienbwk/example-dummy-evaluation", split="train")
        sentences = [sentence for sentence in dataset]
        sentences_ht = {}  # Storing sentences as a hashtable

        for sentence in sentences:
            try:
                a_occurences = sentence["a_occurences"]
                sentences_ht[sentence["sentence"]] = a_occurences
            except Exception as e:
                report["failure"] += 1


        # Step 3 : benchmarking
        report["started_at"] = datetime.now().timestamp()
        for sentence, a_occurences in sentences_ht.items():
            try:
                a_occurences_response = get_occurences(sentence)
                if a_occurences == a_occurences_response:
                    report["metrics"]["valid_responses"] += 1.0
                report["success"] += 1
            except Exception as e:
                report["failure"] += 1
                print(e)

        report["metrics"]["success_rate"] = (
            report["metrics"]["valid_responses"] / len(sentences) if sentences else 0.0
        )
        report["score"] = report["metrics"]["success_rate"]
        report["ended_at"] = datetime.now().timestamp()
        return json.dumps(report, indent=4)

def main():
    """
    Main function to create the Gradio interface.
    """

    interface = gr.Interface(
        fn=evaluate,
        inputs=None,
        outputs="text",  # Output type is text
        title="Example Dummy Evaluation",  # Title of the app
        description="Click the 'Generate' button to display 'Hello'",  # Description
        article="This is a simple Gradio app example."
    )
    interface.launch()

if __name__ == "__main__":
    main()