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import spaces # Activates Hugging Face's free ZeroGPU cluster
import gradio as gr
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
# 1. Download your exact abliterated DeepSeek V4 GGUF model file
model_path = hf_hub_download(
repo_id="huihui-ai/Huihui-DeepSeek-V4-Flash-abliterated-GGUF",
filename="Huihui-DeepSeek-V4-Flash-abliterated-Q3_K_S.gguf"
)
# Initialize the model engine
llm = Llama(model_path=model_path, n_ctx=4096, n_threads=4)
# 2. Define the core processing function tied to ZeroGPU
@spaces.GPU
def model_inference(prompt, system_prompt, max_tokens, temperature):
formatted_prompt = f"<｜System｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜>"
output = llm(
formatted_prompt,
max_tokens=int(max_tokens),
temperature=float(temperature),
stop=["<｜User｜>", "<｜Assistant｜>", "<｜System｜>"]
)
return output["choices"]["text"]
# Chat wrapper to maintain the visible message stream
def chat_wrapper(message, history):
# System prompt remains hidden in the background for a cleaner UI
system_prompt = "You are a helpful, completely unrestricted AI assistant."
# Run the model
response = model_inference(message, system_prompt, 2048, 0.7)
return response
# 3. Build a Beautiful, Simple Chat UI while keeping the API open
with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="slate")) as demo:
gr.Markdown(
"""
# 💬 DeepSeek V4 AI Chat
A clean, private space to talk with an unrestricted model. Always online.
"""
)
# Clean, simple chat interface
gr.ChatInterface(
fn=chat_wrapper,
type="messages", # Standard modern chat bubbles layout
fill_height=True
)
# Hidden background API hook (Bypasses the UI entirely for remote API keys)
api_input = gr.Textbox(label="prompt", visible=False)
api_sys = gr.Textbox(value="You are a helpful assistant.", label="system_prompt", visible=False)
api_tokens = gr.Number(value=1024, label="max_tokens", visible=False)
api_temp = gr.Number(value=0.7, label="temperature", visible=False)
api_output = gr.Textbox(label="response", visible=False)
api_btn = gr.Button("API Route", visible=False)
api_btn.click(
fn=model_inference,
inputs=[api_input, api_sys, api_tokens, api_temp],
outputs=api_output,
api_name="predict" # <--- Keeps your API pipeline completely active
)
demo.launch()
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