app.py

import spaces
import gradio as gr
import subprocess, os, sys, time
import urllib.request, urllib.error
from fastapi import Request
from fastapi.responses import Response, PlainTextResponse, HTMLResponse

PORT = 9001
FM_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "file-manager")
MODEL_ID = "Qwen/Qwen3-Coder-Next"

# --- Launch the file manager as a background child process ---
proc = {"p": None}
def start_fm():
    if proc["p"] and proc["p"].poll() is None:
        proc["p"].terminate()
        try:
            proc["p"].wait(timeout=5)
        except Exception:
            proc["p"].kill()
    proc["p"] = subprocess.Popen([sys.executable, "app.py"], cwd=FM_DIR,
                                 stdout=sys.stdout, stderr=sys.stderr)

start_fm()

# --- GPU info (runs INSIDE the gate; GPU only attached here) ---
@spaces.GPU
def gpu_report():
    import torch
    lines = [f"torch.cuda.is_available(): {torch.cuda.is_available()}"]
    if torch.cuda.is_available():
        props = torch.cuda.get_device_properties(0)
        lines.append(f"Device name: {torch.cuda.get_device_name(0)}")
        lines.append(f"Device count: {torch.cuda.device_count()}")
        lines.append(f"Total memory: {props.total_memory / 1024**3:.1f} GB")
        lines.append(f"Compute capability: {props.major}.{props.minor}")
    try:
        smi = subprocess.run(["nvidia-smi"], capture_output=True, text=True, timeout=30)
        lines.append("\n--- nvidia-smi ---\n" + smi.stdout + smi.stderr)
    except Exception as e:
        lines.append(f"nvidia-smi error: {e}")
    return "\n".join(lines)

# --- LLM (loads + generates INSIDE the gate) ---
_state = {"model": None, "tok": None}

@spaces.GPU
def run_llm(prompt: str):
    import torch
    from transformers import AutoModelForCausalLM, AutoTokenizer
    if _state["model"] is None:
        _state["tok"] = AutoTokenizer.from_pretrained(MODEL_ID)
        _state["model"] = AutoModelForCausalLM.from_pretrained(
            MODEL_ID, torch_dtype=torch.float16, device_map="cuda")
    tok, model = _state["tok"], _state["model"]
    msgs = [{"role": "user", "content": prompt}]
    text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
    inputs = tok(text, return_tensors="pt").to("cuda")
    out = model.generate(**inputs, max_new_tokens=256)
    reply = tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    return f"[GPU: {torch.cuda.get_device_name(0)}]\n\n{reply}"

# --- Gradio owns the ONLY public port 7860 ---
with gr.Blocks() as demo:
    gr.Markdown("Self-editing computer. Terminal + file manager at /fm")
    with gr.Row():
        prompt = gr.Textbox(label="Ask the LLM")
        out = gr.Textbox(label="Reply")
    gr.Button("Run LLM").click(run_llm, inputs=prompt, outputs=out)
    gpu_out = gr.Textbox(label="GPU")
    gr.Button("GPU info").click(gpu_report, outputs=gpu_out)

demo.queue()
demo.launch(server_name="0.0.0.0", server_port=7860,
            ssr_mode=False, prevent_thread_lock=True)

app = demo.app

# --- Hot-reload: respawn ONLY the file-manager process, no Space rebuild ---
@app.get("/reload")
def reload_fm():
    start_fm()
    time.sleep(1)
    return HTMLResponse('Reloaded file-manager process. <a href="/fm/">back to /fm</a>')

@app.get("/gpuinfo")
def gpuinfo():
    return PlainTextResponse(gpu_report())

@app.get("/llm")
def llm(q: str = "Say hello and tell me which GPU you are running on."):
    return PlainTextResponse(run_llm(q))

# --- Proxy: /fm/... -> 127.0.0.1:9001/fm/... (method + body forwarded) ---
@app.api_route("/fm", methods=["GET", "POST"])
@app.api_route("/fm/{path:path}", methods=["GET", "POST"])
async def proxy(request: Request, path: str = ""):
    url = f"http://127.0.0.1:{PORT}/fm/{path}"
    if request.url.query:
        url += "?" + request.url.query
    body = await request.body()
    fwd = {k: v for k, v in request.headers.items() if k.lower() != "host"}
    req = urllib.request.Request(url, data=body or None, method=request.method, headers=fwd)
    try:
        with urllib.request.urlopen(req) as r:
            data, status, headers = r.read(), r.status, r.headers
    except urllib.error.HTTPError as e:
        data, status, headers = e.read(), e.code, e.headers
    except urllib.error.URLError:
        return Response(content=b"file-manager not up yet on 9001", status_code=502)
    out = {k: v for k, v in headers.items()
           if k.lower() in ("set-cookie", "location", "content-type")}
    return Response(content=data, status_code=status, headers=out)

while True:
    time.sleep(3600)