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import gradio as gr
from transformers import pipeline
models = {
"DistilGPT2 SD": "FredZhang7/distilgpt2-stable-diffusion",
"Llama-SmolTalk-3.2-1B": "prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct",
"Dolphin-Phi 3 2.9.2": "cognitivecomputations/dolphin-2.9.2-Phi-3-Medium",
"EXAONE 3.5 2.4B": "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
"Granite 3.3 2B": "ibm-granite/granite-3.3-2b-instruct"
}
def respond(
message,
_: list[tuple[str, str]],
system_prompt: str,
model: str,
max_new_tokens: int,
temperature: float,
top_p: float,
top_k: int
):
pipe = pipeline("text-generation", model=model)
yield pipe(
[
*message,
{
"role": "system",
"content": system_prompt
}
],
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=temperature,
top_p=top_p,
top_k=top_k
)[0]['generated_text']
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
title="Prompt Enhancer Test",
type="messages",
additional_inputs=[
gr.Textbox(value="When the user provides two sentences, return a longer sentence that fuses the two together with a natural motion in between.", lines=5, show_label=True, label="System prompt"),
gr.Radio(list(models.items()), value="FredZhang7/distilgpt2-stable-diffusion", type="value", label="Model"),
# gr.Textbox(value="Enhance the provided text so that it is more vibrant and detailed.", label="System prompt"),
gr.Slider(minimum=8, maximum=128, value=64, step=8, 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",
),
gr.Slider(
minimum=10,
maximum=100,
value=30,
step=5,
label="Top-k",
),
],
)
if __name__ == "__main__":
demo.launch()
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