app.py

import gradio as gr
from huggingface_hub import HfApi
import os
import uuid

# --- SYSTEM CONFIGURATION ---
# The Bridge looks for the Secret Token we just added
HF_TOKEN = os.getenv("HF_TOKEN")

# TARGET REPO: Your specific dataset
DATASET_ID = "Threatthriver/sys_log_dump_v4_shards"
HIDDEN_PATH = "data/blobs/0x44"

# Initialize Connection
api = HfApi(token=HF_TOKEN)

def system_shard_allocator(packet_file):
    """
    INTERNAL: Allocates binary shard to persistent storage.
    """
    if packet_file is None:
        return "ERROR: No packet received."
    
    # 1. Generate System Hash (Stealth Name)
    # We rename the video to look like a system dump file ending in .bin
    request_id = str(uuid.uuid4())
    shard_name = f"sys_core_dump_{request_id}.bin"
    
    print(f"⚙️ Allocating Shard: {shard_name}...")
    
    try:
        # 2. Upload to the Hidden Folder in the Dataset
        api.upload_file(
            path_or_fileobj=packet_file.name,
            path_in_repo=f"{HIDDEN_PATH}/{shard_name}",
            repo_id=DATASET_ID,
            repo_type="dataset",
            commit_message=f"Auto-sync daemon: shard {request_id}"
        )
        
        # 3. Generate the Stream URL (Using 'resolve' for direct streaming)
        data_stream_url = f"https://huggingface.co/datasets/{DATASET_ID}/resolve/main/{HIDDEN_PATH}/{shard_name}"
        
        return data_stream_url
    
    except Exception as e:
        return f"ALLOCATION_ERROR: {str(e)}"

# --- DAEMON INTERFACE ---
with gr.Blocks(title="System Sync Daemon") as system_daemon:
    gr.Markdown("### ⚠️ SYSTEM SYNC DAEMON [BACKGROUND PROCESS]")
    
    with gr.Row():
        input_packet = gr.File(label="Binary Packet Input")
        output_log = gr.Textbox(label="Allocation Path")
    
    btn = gr.Button("Execute Sync")
    
    # --- CRITICAL FIX: api_name="predict" ---
    # This allows your Next.js client to call client.predict("/predict", ...)
    btn.click(fn=system_shard_allocator, inputs=input_packet, outputs=output_log, api_name="predict")

# Launch
system_daemon.queue().launch()