File size: 2,465 Bytes
cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 cdc774e 3547760 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline
# Initialize the InferenceClient with the Zephyr model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Use a pipeline for the ParlBERT model for fill-mask
mask_pipe = pipeline("fill-mask", model="InfAI/parlbert-german-law")
# Define the function for chat completion
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
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})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
# Function to handle mask filling
def fill_mask_function(text):
return mask_pipe(text)
# Create the Gradio interface
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(label="Enter your message"),
gr.State(), # History
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)"),
],
outputs=[
gr.Textbox(label="Response"),
],
title="Zephyr and ParlBERT Chatbot",
description="This chatbot uses Zephyr-7B model and ParlBERT (German Law) for conversation and masked word predictions."
)
# Create a separate Gradio interface for the fill-mask model
mask_demo = gr.Interface(
fn=fill_mask_function,
inputs=gr.Textbox(label="Input text with [MASK] token"),
outputs=gr.JSON(label="Model output"),
live=True,
title="InfAI ParlBERT - German Law",
description="Enter a sentence with a [MASK] token to get predictions from the InfAI ParlBERT model trained on German law text."
)
# Launch both interfaces
if __name__ == "__main__":
demo.launch()
mask_demo.launch()
|