import gradio as gr
from gradio.components import Textbox, Checkbox
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration
from peft import PeftModel
import torch
import datasets
from sentence_transformers import CrossEncoder
import math
import re
from nltk import sent_tokenize, word_tokenize
import nltk
nltk.download('punkt')

# Load bi encoder
# top_k = 10
cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

# Load your fine-tuned model and tokenizer
model_name = "google/flan-t5-large"
peft_name = "legacy107/flan-t5-large-ia3-newsqa-100"
tokenizer = AutoTokenizer.from_pretrained(model_name)
pretrained_model = T5ForConditionalGeneration.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, peft_name)
max_length = 512
max_target_length = 200

# Load your dataset
dataset = datasets.load_dataset("legacy107/newsqa", split="test")
# dataset = dataset.shuffle()
dataset = dataset.select([3, 9, 14, 24, 405, 51, 426, 471, 73, 34, 94, 0])

# Context chunking
def chunk_splitter(context, chunk_size=100, overlap=0.10):
    overlap_size = chunk_size * overlap
    sentences = nltk.sent_tokenize(context)

    chunks = []
    text = sentences[0]

    if len(sentences) == 1:
        chunks.append(text)

    i = 1
    while i < len(sentences):
        text += " " + sentences[i]
        i += 1
        while i < len(sentences) and len(nltk.word_tokenize(f"{text} {sentences[i]}")) <= chunk_size:
            text += " " + sentences[i]
            i += 1

        text = text.replace('\"','"').replace("\'","'").replace('\n\n\n'," ").replace('\n\n'," ").replace('\n'," ")
        chunks.append(text)

        if (i >= len(sentences)):
            break

        j = i - 1
        text = sentences[j]
        while j >= 0 and len(nltk.word_tokenize(f"{sentences[j]} {text}")) <= overlap_size:
            text = sentences[j] + " " + text
            j -= 1

    return chunks


def retrieve_context(query, contexts):
    hits = [{"corpus_id": i} for i in range(len(contexts))]
    cross_inp = [[query, contexts[hit["corpus_id"]]] for hit in hits]
    cross_scores = cross_encoder.predict(cross_inp, show_progress_bar=False)

    for idx in range(len(cross_scores)):
        hits[idx]["cross-score"] = cross_scores[idx]

    hits = sorted(hits, key=lambda x: x["cross-score"], reverse=True)

    return " ".join(
        [contexts[hit["corpus_id"]] for hit in hits]
    ).replace("\n", " ")


# Define your function to generate answers
def generate_answer(question, context, ground, do_pretrained):
    contexts = chunk_splitter(context)
    context = retrieve_context(question, contexts)
    
    # 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

    # Decode the context back
    decoded_context = tokenizer.decode(input_ids[0], skip_special_tokens=True)[len(f"question: {question} context: "):]

    # 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)

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

    return generated_answer, decoded_context, 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 = " | ".join(example["answers"])
        examples.append([question, context, answer, 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")
    ],
    outputs=[
        Textbox(label="Generated Answer"),
        Textbox(label="Retrieved Context"),
        Textbox(label="Pretrained Model's Answer")
    ],
    examples=list_examples(),
    examples_per_page=4
)

# Launch the Gradio interface
iface.launch()