# import gradio as gr
# from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
# from peft import PeftModel, PeftConfig

# # Load tokenizer
# tokenizer = AutoTokenizer.from_pretrained(".")

# # Load base model with quantization
# bnb_config = BitsAndBytesConfig(load_in_4bit=True)
# base_model = AutoModelForCausalLM.from_pretrained(
#     "unsloth/Meta-Llama-3.1-8B-bnb-4bit",  # same base you fine-tuned
#     quantization_config=bnb_config,
#     device_map="auto"
# )

# # Load LoRA adapters
# model = PeftModel.from_pretrained(base_model, ".")

# # Create Gradio Interface
# def generate_response(prompt):
#     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
#     outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7)
#     return tokenizer.decode(outputs[0], skip_special_tokens=True)

# gr.Interface(
#     fn=generate_response,
#     inputs=gr.Textbox(label="Enter your instruction"),
#     outputs=gr.Textbox(label="Model response"),
#     title="LLaMA 3 - Fine-tuned Model"
# ).launch()


import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(".")

# Load base model (non-quantized)
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3-8B",  # use standard non-quantized base model
    device_map="auto"
)

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, ".")

# Create Gradio Interface
def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(label="Enter your instruction"),
    outputs=gr.Textbox(label="Model response"),
    title="LLaMA 3 - Fine-tuned Model"
).launch()