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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,
system_message: str,
max_tokens: int = 128,
temperature: float = 1.0,
top_p: float = 1.0,
):
# Preprocess the input message
input_text = system_message + " " + 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: # Append each token to the accumulated text
generated_text += token
yield generated_text
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
# Define the interface
with gr.Blocks() as demo:
gr.Markdown("# Google Translate-like Interface")
with gr.Row():
with gr.Column():
source_textbox = gr.Textbox(
placeholder="Enter text in English...",
label="Source Text (English)",
lines=5,
)
with gr.Column():
translated_textbox = gr.Textbox(
placeholder="Translation will appear here...",
label="Translated Text (French)",
lines=5,
interactive=False,
)
translate_button = gr.Button("Translate")
with gr.Accordion("Advanced Settings", open=False):
system_message_input = gr.Textbox(
value="translate English to French:",
label="System message",
)
max_tokens_slider = gr.Slider(
minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"
)
temperature_slider = gr.Slider(
minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"
)
top_p_slider = gr.Slider(
minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
)
# Define functionality
translate_button.click(
respond,
inputs=[
source_textbox,
system_message_input,
max_tokens_slider,
temperature_slider,
top_p_slider,
],
outputs=translated_textbox,
)
if __name__ == "__main__":
demo.launch()
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