# -*- coding: utf-8 -*-
"""Untitled18.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1_vTVH3hBX8wVXIgrW1T2Q4N1DSkWoXV8
"""

import os
os.system('pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" --no-deps xformers trl peft accelerate bitsandbytes pytorch')


import gradio as gr

# from transformers import TextStreamer
from unsloth import FastLanguageModel
from google.colab import drive




# Ensure necessary packages are installed



# Define the parameters for the model
max_seq_length = 2048  # Choose any! We auto support RoPE Scaling internally!
dtype = None  # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True  # Use 4bit quantization to reduce memory usage. Can be False.

# Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="lora_model",  # YOUR MODEL YOU USED FOR TRAINING
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
)
FastLanguageModel.for_inference(model)  # Enable native 2x faster inference

# Define the Alpaca prompt
alpaca_prompt = """
### Input:
{}

### Response:
{}"""

# Define the function to generate responses
def chat_alpaca(message: str, history: list, temperature: float, max_new_tokens: int) -> str:
    prompt = alpaca_prompt.format(message, "")
    inputs = tokenizer([prompt], return_tensors="pt").to("cuda")

    # Define the streamer
    # text_streamer = TextStreamer(tokenizer)

    # Generate the response
    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature=temperature)
    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]

    # Return the response
    return response

# Define the response function for the Gradio interface
def respond(message, history, system_message, max_new_tokens, temperature, top_p):
    return chat_alpaca(message, history, temperature, max_new_tokens)

# Create the Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
)

if __name__ == "__main__":
    demo.launch(share=True)