import gradio as gr
from datasets import load_dataset
import evaluate
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer
import numpy as np
import nltk

nltk.download("punkt")
raw_dataset = load_dataset("scientific_papers", "pubmed")
metric = evaluate.load("rouge")
model_checkpoint = "google/flan-t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

if model_checkpoint in ["google/flan-t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b"]:
    prefix = "summarize: "
else:
    prefix = ""

# preprocessing function
max_input_length = 512
max_target_length = 128
def preprocess_function(examples):
    inputs = [prefix + doc for doc in examples["article"]]
    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)

    # Setup the tokenizer for targets
    # with tokenizer.as_target_tokenizer():
    labels = tokenizer(text_target=examples["abstract"], max_length=max_target_length, truncation=True)

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

for split in ["train", "validation", "test"]:
    raw_dataset[split] = raw_dataset[split].select([n for n in np.random.randint(0, len(raw_dataset[split]) - 1, 1_000)])
tokenized_dataset = raw_dataset.map(preprocess_function, batched=True)


model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

batch_size = 8

args = Seq2SeqTrainingArguments(
    f"{model_checkpoint}-scientific_papers",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=1,
    predict_with_generate=True,
    # fp16=True,
    push_to_hub=False,
    gradient_accumulation_steps=2
)

data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)

# computing metrics from the predictions
def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
    # Replace -100 in the labels as we can't decode them.
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
    # Rouge expects a newline after each sentence
    decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
    decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
    # Extract a few results
    result = {key: value * 100 for key, value in result.items()}
        # Add mean generated length
    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]
    result["gen_len"] = np.mean(prediction_lens)
    return {k: round(v, 4) for k, v in result.items()}

trainer = Seq2SeqTrainer(
model,
args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["validation"],
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=compute_metrics
)
trainer.train()

# Define the input and output interface of the app
import gradio as gr

def summarizer(input_text):
    inputs = [prefix + input_text]
    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt")
    summary_ids = model.generate(
        input_ids=model_inputs["input_ids"],
        attention_mask=model_inputs["attention_mask"],
        num_beams=4,
        length_penalty=2.0,
        max_length=max_target_length + 2,  # +2 from original because we start at step=1 and stop before max_length
        repetition_penalty=2.0,
        early_stopping=True,
        use_cache=True
    )
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

# Interface creation and launching
iface = gr.Interface(
    fn=summarizer,
    inputs=gr.inputs.Textbox(label="Input Text"),
    outputs=gr.outputs.Textbox(label="Summary"),
    title="Scientific Paper Summarizer",
    description="Summarizes scientific papers using a fine-tuned T5 model",
    theme="gray"
)
iface.launch()