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#!/usr/bin/env python
import os
from collections.abc import Iterator
from threading import Thread
import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
#
# 1) Custom Pastel Gradient CSS
#
CUSTOM_CSS = """
.gradio-container {
background: linear-gradient(to right, #FFDEE9, #B5FFFC);
}
"""
#
# 2) Description: Add French greeting, plus any info
#
DESCRIPTION = """# Bonjour Dans le chat du consentement
Mistral-7B Instruct Demo
"""
if not torch.cuda.is_available():
DESCRIPTION += (
"\n<p style='color:red;'>Running on CPU - This is likely too large to run effectively.</p>"
)
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
#
# 3) Load Mistral-7B Instruct (requires gating, GPU recommended)
#
if torch.cuda.is_available():
model_id = "mistralai/Mistral-7B-Instruct-v0.3"
tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True # Might be needed for custom code
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
def generate(
message: str,
chat_history: list[dict],
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
"""
This function handles streaming chat text as the model generates it.
Uses Mistral's 'apply_chat_template' to handle chat-style prompting.
"""
conversation = [*chat_history, {"role": "user", "content": message}]
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(
f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens."
)
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(
tokenizer,
timeout=20.0,
skip_prompt=True,
skip_special_tokens=True
)
generate_kwargs = dict(
{"input_ids": input_ids},
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
# Stream partial output as it's generated
yield "".join(outputs)
#
# 4) Build the Chat Interface with extra sliders
#
demo = gr.ChatInterface(
fn=generate,
description=DESCRIPTION,
css=CUSTOM_CSS, # Use our pastel gradient
additional_inputs=[
gr.Slider(
label="Max new tokens",
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
),
gr.Slider(
label="Temperature",
minimum=0.1,
maximum=4.0,
step=0.1,
value=0.6,
),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.9,
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=50,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.2,
),
],
stop_btn=None,
examples=[
["Hello there! How are you doing?"],
["Can you explain briefly what the Python programming language is?"],
["Explain the plot of Cinderella in a sentence."],
["How many hours does it take a man to eat a Helicopter?"],
["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
],
type="messages",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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