import fitz  # PyMuPDF for PDF extraction
import re
import unsloth
import os
from huggingface_hub import login
from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model
import gradio as gr
from transformers import pipeline


def extract_text_from_pdf(pdf_path):
    """Extract text from a PDF file"""
    doc = fitz.open(pdf_path)
    text = "\n".join([page.get_text("text") for page in doc])
    return text.strip()

def preprocess_text(text):
    """Basic text preprocessing"""
    return re.sub(r"\s+", " ", text).strip()

pdf_text = extract_text_from_pdf("new-american-standard-bible.pdf")
clean_text = preprocess_text(pdf_text)


# Read the Hugging Face token from environment variables
hf_token = os.getenv("access_token")

if hf_token is None:
    raise ValueError("'access_token' is not set. Add it as a secret variable in Hugging Face Spaces.")

# Log in to Hugging Face
login(token=hf_token)

#model_name = "meta-llama/Llama-2-7b-hf"  # You can use a smaller one like "meta-llama/Llama-2-7b-chat-hf"
model_name = "unsloth/llama-2-7b-chat"

tokenizer = AutoTokenizer.from_pretrained(model_name)

# Create dataset
data = {"text": [clean_text]}
dataset = Dataset.from_dict(data)

# Set a padding token manually
tokenizer.pad_token = tokenizer.eos_token  # Use EOS as PAD token
# Alternatively, add a new custom pad token
# tokenizer.add_special_tokens({'pad_token': '[PAD]'})

# Tokenization function
def tokenize_function(examples):
    tokens = tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
    tokens["labels"] = tokens["input_ids"].copy()  # Use input as labels for text generation
    return tokens

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Load LLaMA 2 model in 4-bit mode to save memory
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    load_in_4bit=True,  # Use 4-bit quantization for efficiency
    device_map="auto"
    #device_map="cpu",
    #quantization_config=None
)

# Apply LoRA (efficient fine-tuning)
lora_config = LoraConfig(
    r=8,  # Low-rank parameter
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],  # Applies only to attention layers
    lora_dropout=0.05
)

model = get_peft_model(model, lora_config)

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="no",   # Disable evaluation (to enable, change value to 'epoch')
    learning_rate=2e-4,
    per_device_train_batch_size=1,  # Reduce batch size for memory efficiency
    per_device_eval_batch_size=1,
    num_train_epochs=3,
    weight_decay=0.01,
    save_strategy="epoch",
    logging_dir="./logs",
    logging_steps=10,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets,
    tokenizer=tokenizer,
)

def perform_training():
    trainer.train()

perform_training()

model.save_pretrained("./fine_tuned_llama2")
tokenizer.save_pretrained("./fine_tuned_llama2")


# CHATBOT START
chatbot = pipeline("text-generation", model="./fine_tuned_llama2")

def chatbot_response(prompt):
    result = chatbot(prompt, max_length=100, do_sample=True, temperature=0.7)
    return result[0]["generated_text"]

iface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text")
iface.launch()