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import gradio as gr
from llama_cpp import Llama
from huggingface_hub import snapshot_download
import os
# Baixe o GGUF direto do Hub no runtime (pula limite de 1GB upload)
MODEL_REPO = "mradermacher/DeepHat-V1-7B-GGUF"
MODEL_FILE = "DeepHat-V1-7B.Q4_K_M.gguf" # ~4.8GB, baixa uma vez e cacheia
LOCAL_PATH = "./models/" # Pasta local no Space
# Função pra carregar modelo (roda na init)
def load_model():
os.makedirs(LOCAL_PATH, exist_ok=True)
model_path = snapshot_download(
repo_id=MODEL_REPO,
filename=MODEL_FILE,
local_dir=LOCAL_PATH,
local_dir_use_symlinks=False # Evita links quebrados
)
full_path = os.path.join(model_path, MODEL_FILE)
llm = Llama(
model_path=full_path,
n_ctx=2048,
n_threads=4,
verbose=False
)
return llm
# Carregue na init (leva ~5-10 min na primeira build, depois cache)
print("Baixando DeepHat... (pode demorar na CPU)")
llm = load_model()
def generate_response(prompt, max_tokens=500):
full_prompt = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
output = llm(
full_prompt,
max_tokens=max_tokens,
temperature=0.7,
top_p=0.9,
stop=["<|im_end|>", "</s>"]
)
return output['choices'][0]['text'].strip()
# Interface Gradio
with gr.Blocks(title="DeepHat Uncensored Chat") as demo:
gr.Markdown("# DeepHat - IA Uncensored pra Cibersegurança & Hacking Ético")
chatbot = gr.Chatbot()
msg = gr.Textbox(placeholder="Pergunte sobre hacking WiFi, pentest ou censura...")
clear = gr.Button("Clear")
def respond(message, chat_history):
bot_message = generate_response(message)
chat_history.append((message, bot_message))
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
if __name__ == "__main__":
demo.launch() |