import gradio as gr
from gradio.components import Textbox, Checkbox
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration
from peft import PeftModel, PeftConfig
import torch
import datasets

# Load your fine-tuned model and tokenizer
model_name = "google/flan-t5-large"
peft_name = "legacy107/flan-t5-large-ia3-cpgQA"
tokenizer = AutoTokenizer.from_pretrained(model_name)
pretrained_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
model = PeftModel.from_pretrained(pretrained_model, peft_name)

peft_name = "legacy107/flan-t5-large-ia3-bioasq-paraphrase"
peft_config = PeftConfig.from_pretrained(peft_name)
paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
paraphrase_model = PeftModel.from_pretrained(paraphrase_model, peft_name)

max_length = 512
max_target_length = 200

# Load your dataset
dataset = datasets.load_dataset("minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent", split="test")
dataset = dataset.shuffle()
dataset = dataset.select(range(10))


def paraphrase_answer(question, answer):
    # Combine question and context
    input_text = f"question: {question}. Paraphrase the answer to make it more natural answer: {answer}"

    # Tokenize the input text
    input_ids = tokenizer(
        input_text,
        return_tensors="pt",
        padding="max_length",
        truncation=True,
        max_length=max_length,
    ).input_ids

    # Generate the answer
    with torch.no_grad():
        generated_ids = paraphrase_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)

    # Decode and return the generated answer
    paraphrased_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)

    return paraphrased_answer


# Define your function to generate answers
def generate_answer(question, context, ground_truth, do_pretrained, do_natural):
    # Combine question and context
    input_text = f"question: {question} context: {context}"

    # Tokenize the input text
    input_ids = tokenizer(
        input_text,
        return_tensors="pt",
        padding="max_length",
        truncation=True,
        max_length=max_length,
    ).input_ids

    # Generate the answer
    with torch.no_grad():
        generated_ids = model.generate(input_ids=input_ids, max_new_tokens=max_target_length)

    # Decode and return the generated answer
    generated_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)

    # Paraphrase answer
    paraphrase_answer = ""
    if do_natural:
        paraphrased_answer = paraphrase_answer(question, generated_answer)

    # Get pretrained model's answer
    pretrained_answer = ""
    if do_pretrained:
        with torch.no_grad():
            generated_ids = pretrained_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
            pretrained_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)

    return generated_answer, paraphrased_answer, pretrained_answer


# Define a function to list examples from the dataset
def list_examples():
    examples = []
    for example in dataset:
        context = example["context"]
        question = example["question"]
        answer = example["answer_text"]
        examples.append([question, context, answer, True, True])
    return examples


# Create a Gradio interface
iface = gr.Interface(
    fn=generate_answer,
    inputs=[
        Textbox(label="Question"),
        Textbox(label="Context"),
        Textbox(label="Ground truth"),
        Checkbox(label="Include pretrained model's result"),
        Checkbox(label="Include natural answer")
    ],
    outputs=[
        Textbox(label="Generated Answer"),
        Textbox(label="Natural Answer"),
        Textbox(label="Pretrained Model's Answer"),
    ],
    examples=list_examples()
)

# Launch the Gradio interface
iface.launch()