import gradio as gr
from huggingface_hub import InferenceClient
import os

"""
Copied from inference in colab notebook
"""

from transformers import TextIteratorStreamer , pipeline
from threading import Thread

# Load model and tokenizer globally to avoid reloading for every request
model_path = "Mat17892/t5small_enfr_opus"

translator = pipeline("translation_xx_to_yy", model=model_path)

def respond(
    message: str,
    history: list[tuple[str, str]],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    message = "translate English to French:" + message

    response = translator(message)
    print(response)
    yield response

# def respond(
#     message: str,
#     history: list[tuple[str, str]],
#     system_message: str,
#     max_tokens: int,
#     temperature: float,
#     top_p: float,
# ):
#     # Combine system message and history into a single prompt
#     messages = [{"role": "system", "content": system_message}]
#     for val in history:
#         if val[0]:
#             messages.append({"role": "user", "content": val[0]})
#         if val[1]:
#             messages.append({"role": "assistant", "content": val[1]})
#     messages.append({"role": "user", "content": message})
    
#     # Tokenize the messages
#     inputs = tokenizer.apply_chat_template(
#         messages,
#         tokenize = True,
#         add_generation_prompt = True, # Must add for generation
#         return_tensors = "pt",
#     )
#     # Generate tokens incrementally
#     streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
#     generation_kwargs = {
#         "input_ids": inputs,
#         "max_new_tokens": max_tokens,
#         "temperature": temperature,
#         "top_p": top_p,
#         "do_sample": True,
#         "streamer": streamer,
#     }
#     thread = Thread(target=model.generate, kwargs=generation_kwargs)
#     thread.start()

#     # Yield responses as they are generated
#     response = ""
#     for token in streamer:
#         response += token
#         yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
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()