import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize
#from tiny_llama import generate_answer
#from llama_generate import generate_answer
from cpu_llama_generate import generate_answer

def get_yes_or_no(result):
    if 'yes' in str.lower(result)[:5]:return 'Yes'
    if 'no' in str.lower(result)[:5]:return 'No'
    return 'N/A'


def check_score(context, sentences):
    score_mapping = {'Yes':1.0, 'No':0.0}
    template = """
        Context: {a}
        Sentence: {b}
        is the sentence supported by the context above? 
        Answer "Yes" or "No"
    """
    scores, results = list(), list()
    for sentence in sentences:
        content = template.format(a=context.strip().replace('/n', ''), b=sentence.strip().replace('/n', ''))
        result = generate_answer(content, sample_num=1)[0]
        print(result)
        results.append(result)

    results = [get_yes_or_no(r) for r in results]
    scores = [score_mapping.get(result, 0.5) for result in results]

    # for sent, score in zip(sentences, scores):
    #     print(sent.strip(), score)
        #result_string += sent + ' ({a})'.format(a=score)

    return scores


def run(query, sample_size=5):
    sampled = generate_answer(query, sample_size+1)
    answer = sampled[0]
    proofs = sampled[1:]
    sentences = sent_tokenize(answer)

    all_scores = list()
    for proof in proofs:
        scores = check_score(proof, sentences)
        all_scores.append(scores)

    final_content = ''
    avg_confidence = list()
    for index, scores in enumerate(zip(*all_scores)):
        sentence_confidence = sum(scores) / len(scores)
        avg_confidence.append(sentence_confidence)
        final_content += sentences[index].strip() + ' ({a}) '.format(a=sentence_confidence)
    avg_confidence = sum(avg_confidence) / len(avg_confidence)
    final_content += '\nThe confidence score of this answer is {a}'.format(a=avg_confidence)
    return final_content


if __name__ == '__main__':
    # result = generate_answer(query="Who is Lihu Chen?", sample_num=3)
    # print(result)

    # context = """
    # Lihu Chen is an American writer and artist who works in comics. They received their degree in psychology from California State University, Fullerton and have worked on titles such as "The Gathering Storm" and "Heartthrob".
    # """
    # sentences = sent_tokenize("""
    # Lihu Chen is an American writer and artist who works in comics. They received their degree in psychology from California State University, Fullerton and have worked on titles such as "The Gathering Storm" and "Heartthrob".
    # """)
    # result = check_score(context, sentences)
    # print(result)
    # result = """

    answer = run(query='who is Lihu Chen', sample_size=5)
    print(answer)