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import gradio as gr
from huggingface_hub import InferenceClient
import os
"""
Copied from inference in colab notebook
"""
from transformers import pipeline
# 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)[0]
# yield response['translation_text']
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TextIteratorStreamer
import threading
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
def respond(
message: str,
history: list[tuple[str, str]],
system_message: str,
max_tokens: int = 128,
temperature: float = 1.0,
top_p: float = 1.0,
):
# Preprocess the input message
input_text = "translate English to French: " + message
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
# Set up the streamer
streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
# Generate in a separate thread to avoid blocking
generation_thread = threading.Thread(
target=model.generate,
kwargs={
"input_ids": input_ids,
"max_new_tokens": max_tokens,
"do_sample": True,
"temperature": temperature,
"top_p": top_p,
"streamer": streamer,
},
)
generation_thread.start()
# Stream the output progressively
generated_text = ""
for token in streamer:
generated_text += token # Append each token to the accumulated text
yield generated_text
"""
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)",
# ),
# ],
# )
demo = gr.Interface(fn=respond,
inputs=[gr.Textbox(label="Input Text", placeholder="Input Text To Be Translated")],
outputs=gr.Textbox(label="Translation"),
title="tTranslatorR-Opus"
)
if __name__ == "__main__":
demo.launch()
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