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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import torch
# ←–– set this to the exact name of your HF repo
HF_MODEL_ID = "rieon/DeepCoder-14B-Preview-Suger"
# explicitly tell the client you want text-generation
# client = InferenceClient(model=HF_MODEL_ID)
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_ID, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
HF_MODEL_ID,
device_map="auto", # spreads across all available GPUs
torch_dtype=torch.float16
)
model.eval()
# def respond(
# message: str,
# history: list[dict], # [{"role":"user"/"assistant","content":…}, …]
# system_message: str,
# max_tokens: int,
# temperature: float,
# top_p: float,
# ):
# # 1️⃣ Build one raw-text prompt from system + chat history + new user turn
# prompt = system_message.strip() + "\n"
# for msg in history:
# role = msg["role"]
# content = msg["content"]
# if role == "user":
# prompt += f"User: {content}\n"
# elif role == "assistant":
# prompt += f"Assistant: {content}\n"
# prompt += f"User: {message}\nAssistant:"
# # 2️⃣ Stream tokens from the text-generation endpoint
# generated = ""
# for chunk in client.text_generation(
# prompt, # first positional arg
# max_new_tokens=max_tokens,
# temperature=temperature,
# top_p=top_p,
# stream=True,
# ):
# generated += chunk.generated_text
# yield generated
def respond(
message: str,
history: list[dict],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
):
# assemble a single prompt from system message + history
prompt = system_message.strip() + "\n"
# for user, bot in history:
# prompt += f"User: {user}\nAssistant: {bot}\n"
prompt += f"User: {message}\nAssistant:"
# stream back tokens
# generated = ""
# for chunk in client.text_generation(
# prompt,
# max_new_tokens=max_tokens,
# temperature=temperature,
# top_p=top_p,
# stream=True,
# ):
# # the API returns a small JSON with .generated_text
# generated += chunk.generated_text
# yield generated
streamer = TextIteratorStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
inputs = tokenizer(prompt, return_tensors="pt").to(device)
model.generate(**inputs,
streamer=streamer,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p)
output = ""
for tok in streamer:
output += tok
yield output
demo = gr.ChatInterface(
fn=respond,
type="messages",
title="DeepCoder with Suger",
description="Upload any text or pdf files and ask questions about them!",
additional_inputs=[
gr.Textbox(value="You are a helpful coding assistant.", label="System message"),
gr.Slider(1, 2048, value=512, step=1, label="Max new tokens"),
gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p"),
],
)
if __name__ == "__main__":
demo.launch()
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