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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()
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