import gradio as gr
from unsloth import FastLanguageModel
from transformers import TextStreamer
import torch

# Function to load the model
def load_model(model_name, max_seq_length, dtype, load_in_4bit, token=None):
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name=model_name,
        max_seq_length=max_seq_length,
        dtype=dtype,
        load_in_4bit=load_in_4bit,
        token=token
    )
    FastLanguageModel.for_inference(model)  # Enable native 2x faster inference
    return model, tokenizer

# Load the model
model_name = "unsloth/Phi-3-mini-4k-instruct"
token = None  # Replace with your token if required

model, tokenizer = load_model(model_name, max_seq_length=2048, dtype=None, load_in_4bit=True, token=token)

def generate_response(instruction, input_text, max_new_tokens):
    alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

    ### Instruction:
    {}

    ### Input:
    {}

    ### Response:
    {}"""

    inputs = tokenizer(
        [
            alpaca_prompt.format(
                instruction,  # instruction
                input_text,   # input
                ""            # output - leave this blank for generation!
            )
        ], return_tensors="pt").to("cpu")

    text_streamer = TextStreamer(tokenizer)
    output = model.generate(**inputs, streamer=text_streamer, max_new_tokens=max_new_tokens)

    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

# Gradio Interface
iface = gr.Interface(
    fn=generate_response,
    inputs=[
        gr.Textbox(lines=2, label="Instruction", placeholder="Continue the Fibonacci sequence."),
        gr.Textbox(lines=2, label="Input", placeholder="1, 1, 2, 3, 5, 8"),
        gr.Slider(1, 2048, value=128, step=1, label="Max New Tokens")
    ],
    outputs=gr.Textbox(label="Response", lines=10),
    title="Language Model Chat UI"
)

iface.launch()