import gradio as gr
import torch
import pandas as pd
import bitsandbytes as bnb
import evaluate
import numpy as np
import random
import huggingface_hub
import os
from datasets import Dataset, DatasetDict, load_dataset
from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model
from transformers import (AutoTokenizer, BitsAndBytesConfig, TrainingArguments, AutoModelForSequenceClassification, Trainer, EarlyStoppingCallback, DataCollatorWithPadding)
from huggingface_hub import login

def process(model_id, dataset):
    # Step 1: Load dataset
    dataset_imdb = load_dataset(dataset)

    # Step 2: Reduce dataset (optional)

    reduction_rate = 0.1
    num_train_to_keep = int(reduction_rate * dataset_imdb["train"].num_rows)
    num_test_to_keep = int(reduction_rate * dataset_imdb["test"].num_rows)
    
    def select_random_indices(dataset, num_to_keep):
        indices = list(range(dataset.num_rows))
        random.shuffle(indices)
        return indices[:num_to_keep]
    
    train_indices = select_random_indices(dataset_imdb["train"], num_train_to_keep)
    test_indices = select_random_indices(dataset_imdb["test"], num_test_to_keep)
    
    dataset_imdb = DatasetDict({
        "train": dataset_imdb["train"].select(train_indices),
        "test": dataset_imdb["test"].select(test_indices),
    })

    # Step 3: Text tokenization

    def preprocess_function(examples):
        return tokenizer(examples["text"], padding="max_length", truncation=True)
    
    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")

    # Step 4: Apply tokenization to dataset

    tokenized_imdb = dataset_imdb.map(preprocess_function, batched=True)

    #Step 5: Fine-tune the model

    login(token=os.environ.get("HF_TOKEN"))
    
    model_id = model_id
    model = AutoModelForSequenceClassification.from_pretrained(model_id)
    
    lora_config = LoraConfig(task="sequence_classification")
    peft_model = PeftModel(model, lora_config)
    
    training_args = TrainingArguments(
        output_dir="./results",
        num_train_epochs=3,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=64,
        evaluation_strategy="epoch",
        learning_rate=1e-5,
        save_total_limit=2,
        save_steps=500,
        load_best_model_at_end=True,
        metric_for_best_model="accuracy",
        greater_is_better=True,
        save_strategy="steps",
        eval_accumulation_steps=10,
    )
    
    trainer = Trainer(
        model=peft_model,
        args=training_args,
        train_dataset=tokenized_imdb["train"],
        eval_dataset=tokenized_imdb["test"],
        compute_metrics=lambda pred: {"accuracy": torch.sum(pred.label_ids == pred.predictions.argmax(-1)).item()},
        data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
    )
    
    trainer.train()

    # Step 6: Evaluate the fine-tuned model

    targets = []
    predictions = []
    for i in range(len(tokenized_imdb["test"])):
        review = tokenized_imdb["test"][i]["text"]
        target_sentiment = tokenized_imdb["test"][i]["label"]
        predicted_sentiment = predict_sentiment(review)
        if predicted_sentiment in ["positive", "negative"]:
            targets.append(target_sentiment)
            predictions.append(predicted_sentiment)
        print(f"Record {i+1} - Actual: {target_sentiment}, Predicted: {predicted_sentiment}")
    
    return "Done"

demo = gr.Interface(fn=process, 
                    inputs=[gr.Textbox(label = "Model ID", value = "codellama/CodeLlama-7b-hf", lines = 1),
                            gr.Textbox(label = "Dataset", value = "open-assistance/prompting", lines = 1)],
                    outputs=[gr.Textbox(label = "Completion")])
demo.launch()