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from huggingface_hub import InferenceClient
import gradio as gr
import pandas as pd
# Inference client initialization
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
# Function to format the prompt
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
# Function to generate text based on prompt and history
def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
# Format the prompt
formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
# Generate text using InferenceClient
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
return output
# Additional input components for Gradio interface
additional_inputs=[
gr.File(label="Upload CSV or Document", type="upload"), # Max file size is 2 GB
gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs"),
gr.Slider(label="Max new tokens", value=256, minimum=0, maximum=5120, step=64, interactive=True, info="The maximum numbers of new tokens"),
gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens"),
gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens")
]
# Function to read uploaded CSV or Document
def read_file(file):
if file is None:
return None
elif file.name.endswith('.csv'):
return pd.read_csv(file)
elif file.name.endswith('.txt'):
with open(file.name, 'r') as f:
return f.read()
# Gradio Chat Interface
gr.ChatInterface(
fn=generate,
inputs=[
gr.Textbox(label="Prompt"),
gr.Textbox(label="History", placeholder="User1: Hello\nBot: Hi there!\nUser1: How are you?"),
gr.Textbox(label="System Prompt"),
],
outputs=gr.Textbox(label="Response"),
title="Synthetic-data-generation-aze",
additional_inputs=additional_inputs,
examples=[
["What is the capital of France?", "Paris", "Ask me anything"],
["How are you?", "I'm good, thank you!", "User"],
],
allow_flagging=False,
allow_upvoting=False,
allow_duplicate_of_same_input=False,
flagging_options=["Inappropriate", "Incorrect", "Offensive"],
thumbs=None,
).launch()
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