File size: 4,352 Bytes
ce72fd9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | import os
import gradio as gr
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
# 1. Fetch your secret credentials from the environment
ADMIN_USER = os.environ.get("ADMIN_USER")
ADMIN_PASS = os.environ.get("ADMIN_PASS")
# 2. Define the path inside your persistent storage for Phi-4 Mini
model_path = "/data/Phi-4-mini-instruct-Q4_K_M.gguf"
# Auto-download from the UNGATED Unsloth public repository if missing
if not os.path.exists(model_path):
print("\n📦 Initializing Gated-Bypass CPU Model Setup...")
hf_hub_download(
repo_id="unsloth/Phi-4-mini-instruct-GGUF",
filename="Phi-4-mini-instruct-Q4_K_M.gguf",
local_dir="/data"
)
print("✅ Download complete!\n")
print("Loading model from persistent storage...")
llm = Llama(
model_path=model_path,
n_ctx=2048,
n_threads=2,
n_batch=512,
verbose=False
)
# 3. Define the Chat Engine Logic
def respond(message, history):
messages = [
{"role": "system", "content": "You are a helpful, intelligent AI assistant. Keep responses concise."}
]
# ROBUST GRADIO 6.0 HISTORY PARSING
# Iterates over individual message objects rather than historic pairs
for msg in history:
if hasattr(msg, "role") and hasattr(msg, "content"):
role = msg.role
content = msg.content
elif isinstance(msg, dict):
role = msg.get("role")
content = msg.get("content")
else:
continue
if role and content:
messages.append({"role": str(role).lower(), "content": str(content)})
# Safe current user text extraction (handles strings, objects, or dict messages)
user_text = message
if hasattr(message, "content"):
user_text = message.content
elif isinstance(message, dict) and "content" in message:
user_text = message["content"]
messages.append({"role": "user", "content": str(user_text)})
response = llm.create_chat_completion(
messages=messages,
max_tokens=512,
temperature=0.7,
stream=True
)
partial_message = ""
for chunk in response:
if "choices" in chunk and len(chunk["choices"]) > 0:
delta = chunk["choices"][0].get("delta", {})
if "content" in delta:
partial_message += delta["content"]
yield partial_message
# 4. Build the Application with custom Login Gate Routing
with gr.Blocks() as demo:
has_auth = bool(ADMIN_USER and ADMIN_PASS)
# CONTAINER A: The Secure Login Box UI
with gr.Column(visible=has_auth) as login_container:
gr.Markdown("# 🔒 Protected Workspace\nPlease enter your administrator credentials to access the CPU environment.")
username_input = gr.Textbox(label="Username", placeholder="Enter username...")
password_input = gr.Textbox(label="Password", type="password", placeholder="Enter password...")
login_button = gr.Button("Access System", variant="primary")
error_output = gr.Markdown(visible=False)
# CONTAINER B: The Main Chat Application UI
with gr.Column(visible=not has_auth) as app_container:
gr.ChatInterface(
fn=respond,
title="Phi-4 Mini 3.8B Chat (CPU Optimized)",
description="Running instantly via persistent local storage with custom 2 vCPU optimizations."
)
# Core Verification Function
def handle_login(username, password):
if username == ADMIN_USER and password == ADMIN_PASS:
return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
else:
return gr.update(visible=True), gr.update(visible=False), gr.update(value="❌ **Access Denied:** Invalid username or password.", visible=True)
# Link button click to verification function
login_button.click(
fn=handle_login,
inputs=[username_input, password_input],
outputs=[login_container, app_container, error_output]
)
# 5. Launch the Space safely without launcher auth conflicts
if __name__ == "__main__":
# Theme configuration parameter handled at launch level to comply with Gradio 6 updates
demo.launch(server_name="0.0.0.0", server_port=7860, theme="soft") |