app.py

from __future__ import annotations

import json
import os
import re
from typing import Any, Dict, List, Tuple

import gradio as gr
import spaces
import torch
from transformers import AutoTokenizer, TextIteratorStreamer, pipeline
from threading import Thread

# Enable optimizations
torch.backends.cuda.matmul.allow_tf32 = True

# Try to import vLLM (primary inference engine)
try:
    from vllm import LLM, SamplingParams
    from vllm.engine.arg_utils import AsyncEngineArgs
    VLLM_AVAILABLE = True
except ImportError:
    VLLM_AVAILABLE = False
    LLM = None
    SamplingParams = None
    print("Warning: vLLM not available, falling back to Transformers")

# Try to import LLM Compressor (for quantization)
try:
    from llmcompressor import oneshot
    from llmcompressor.modifiers.quantization import AWQModifier
    LLM_COMPRESSOR_AVAILABLE = True
except ImportError:
    LLM_COMPRESSOR_AVAILABLE = False
    print("Warning: LLM Compressor not available (models should be pre-quantized)")

# Try to import AWQ (deprecated, but kept for fallback compatibility)
# Note: AutoAWQ is deprecated; vLLM handles AWQ natively via llm-compressor
try:
    from awq import AutoAWQForCausalLM
    AWQ_AVAILABLE = True
    import warnings
    warnings.filterwarnings("ignore", category=DeprecationWarning, module="awq")
except ImportError:
    AWQ_AVAILABLE = False
    print("Info: AutoAWQ not available (using vLLM native AWQ support instead)")

# Always import BitsAndBytesConfig for fallback
try:
    from transformers import BitsAndBytesConfig
    BITSANDBYTES_AVAILABLE = True
except ImportError:
    BITSANDBYTES_AVAILABLE = False
    BitsAndBytesConfig = None
    print("Warning: BitsAndBytes not available")

# Try to import FlashAttention-2
try:
    import flash_attn
    FLASH_ATTN_AVAILABLE = True
except ImportError:
    FLASH_ATTN_AVAILABLE = False
    print("Warning: FlashAttention-2 not available")

HF_TOKEN = os.environ.get("HF_TOKEN")
if not HF_TOKEN:
    raise RuntimeError("HF_TOKEN environment variable must be set for private router checkpoints.")

PLAN_END_TOKEN = "<|end_of_plan|>"
STOP_SEQUENCES = [PLAN_END_TOKEN, "</json>", "</JSON>"]

ROUTER_SYSTEM_PROMPT = """You are the Router Agent coordinating Math, Code, and General-Search specialists.\nEmit EXACTLY ONE strict JSON object with keys route_plan, route_rationale, expected_artifacts,\nthinking_outline, handoff_plan, todo_list, difficulty, tags, acceptance_criteria, metrics.\nRules:\n- No markdown/code fences, no natural-language prologues or epilogues.\n- route_plan must be an ordered list of tool invocations such as /math(...), /code(...), /general-search(...).\n- todo_list must map each checklist item to the responsible tool.\n- metrics must include primary and secondary arrays (add optional *_guidance fields when they exist).\n- After the closing brace of the JSON object, immediately append the sentinel <|end_of_plan|>.\nExample output:\n{\n  "route_plan": ["/general-search(...)"],\n  "route_rationale": "...",\n  ...\n}<|end_of_plan|>\nReturn nothing else."""

MODELS = {
    "Router-Qwen3-32B-AWQ": {
        "repo_id": "Alovestocode/router-qwen3-32b-merged",
        "description": "Router checkpoint on Qwen3 32B merged, optimized with AWQ quantization via vLLM.",
        "params_b": 32.0,
        "quantization": "awq",  # vLLM will auto-detect AWQ
    },
    "Router-Gemma3-27B-AWQ": {
        "repo_id": "Alovestocode/router-gemma3-merged",
        "description": "Router checkpoint on Gemma3 27B merged, optimized with AWQ quantization via vLLM.",
        "params_b": 27.0,
        "quantization": "awq",  # vLLM will auto-detect AWQ
    },
}

REQUIRED_KEYS = [
    "route_plan",
    "route_rationale",
    "expected_artifacts",
    "thinking_outline",
    "handoff_plan",
    "todo_list",
    "difficulty",
    "tags",
    "acceptance_criteria",
    "metrics",
]

PIPELINES: Dict[str, Any] = {}  # For Transformers fallback
VLLM_MODELS: Dict[str, Any] = {}  # For vLLM models
TOKENIZER_CACHE: Dict[str, Any] = {}
WARMED_REMAINING = False
TOOL_PATTERN = re.compile(r"^/[a-z0-9_-]+\(.*\)$", re.IGNORECASE)


def get_tokenizer(repo: str):
    tok = TOKENIZER_CACHE.get(repo)
    if tok is not None:
        return tok
    tok = AutoTokenizer.from_pretrained(
        repo, 
        token=HF_TOKEN,
        use_fast=True,
        trust_remote_code=True
    )
    tok.padding_side = "left"
    tok.truncation_side = "left"
    if tok.pad_token_id is None and tok.eos_token_id is not None:
        tok.pad_token_id = tok.eos_token_id
    TOKENIZER_CACHE[repo] = tok
    return tok


def load_vllm_model(model_name: str):
    """Load model with vLLM (supports AWQ natively, continuous batching, PagedAttention)."""
    if model_name in VLLM_MODELS:
        return VLLM_MODELS[model_name]
    
    repo = MODELS[model_name]["repo_id"]
    model_config = MODELS[model_name]
    quantization = model_config.get("quantization", None)
    
    print(f"Loading {repo} with vLLM (quantization: {quantization})...")
    
    try:
        # vLLM configuration optimized for ZeroGPU H200 slice
        # vLLM natively supports AWQ via llm-compressor (replaces deprecated AutoAWQ)
        llm_kwargs = {
            "model": repo,
            "trust_remote_code": True,
            "token": HF_TOKEN,
            "dtype": "bfloat16",  # Prefer bf16 over int8 for speed
            "gpu_memory_utilization": 0.90,  # Leave headroom for KV cache
            "max_model_len": 16384,  # Adjust based on GPU memory
            "enable_chunked_prefill": True,  # Better for long prompts
            "tensor_parallel_size": 1,  # Single GPU for ZeroGPU
            "max_num_seqs": 256,  # Continuous batching capacity
            "enable_prefix_caching": True,  # Cache prompts for faster TTFT
        }
        
        # Add quantization if specified (vLLM auto-detects AWQ via llm-compressor)
        if quantization == "awq":
            llm_kwargs["quantization"] = "awq"
            # vLLM will auto-detect AWQ weights if present (handled by llm-compressor)
            print(f"  → AWQ quantization enabled (vLLM native support)")
        
        print(f"  → Loading with vLLM (continuous batching, PagedAttention)...")
        llm = LLM(**llm_kwargs)
        VLLM_MODELS[model_name] = llm
        print(f"✅ vLLM model loaded: {model_name}")
        print(f"   - Continuous batching: enabled (max {llm_kwargs['max_num_seqs']} concurrent)")
        print(f"   - Prefix caching: enabled")
        print(f"   - Quantization: {quantization or 'none (bf16)'}")
        return llm
    except Exception as exc:
        print(f"❌ vLLM load failed for {repo}: {exc}")
        import traceback
        traceback.print_exc()
        raise


def load_awq_pipeline(repo: str, tokenizer):
    """Load AWQ-quantized model with FlashAttention-2 and torch.compile (Transformers fallback)."""
    model = AutoAWQForCausalLM.from_quantized(
        repo,
        fuse_layers=True,
        trust_remote_code=True,
        device_map="auto",
        token=HF_TOKEN,
    )
    
    # Prepare model kwargs with FlashAttention-2 if available
    model_kwargs = {}
    if FLASH_ATTN_AVAILABLE:
        model_kwargs["attn_implementation"] = "flash_attention_2"
    
    pipe = pipeline(
        task="text-generation",
        model=model,
        tokenizer=tokenizer,
        trust_remote_code=True,
        device_map="auto",
        model_kwargs=model_kwargs,
        use_cache=True,
        torch_dtype=torch.bfloat16,  # Prefer bf16 over int8 for speed
    )
    pipe.model.eval()
    
    # Apply torch.compile for kernel fusion (~10-20% speedup after first call)
    try:
        if hasattr(torch, 'compile'):
            print("Applying torch.compile for kernel fusion...")
            pipe.model = torch.compile(pipe.model, mode="reduce-overhead")
            print("✅ torch.compile applied (first call will be slower, subsequent calls faster)")
    except Exception as exc:
        print(f"⚠️ torch.compile failed: {exc} (continuing without compilation)")
    
    return pipe


def load_pipeline(model_name: str):
    """Load model with vLLM (preferred) or Transformers (fallback).
    
    Fallback chain:
    1. vLLM with AWQ (best performance, continuous batching)
    2. vLLM with FP16 (if AWQ not available)
    3. Transformers with AWQ (via AutoAWQ - deprecated but functional)
    4. Transformers with BitsAndBytes 8-bit
    5. Transformers with FP16/FP32
    """
    # Try vLLM first (best performance with native AWQ support via llm-compressor)
    # vLLM handles AWQ natively, so AutoAWQ deprecation doesn't affect us
    if VLLM_AVAILABLE:
        try:
            print(f"🔄 Attempting to load {model_name} with vLLM (native AWQ support)...")
            return load_vllm_model(model_name)
        except Exception as exc:
            print(f"⚠️ vLLM load failed: {exc}")
            print(f"   → Falling back to Transformers pipeline...")
            import traceback
            traceback.print_exc()
    
    # Fallback to Transformers pipeline
    if model_name in PIPELINES:
        print(f"✅ Using cached Transformers pipeline for {model_name}")
        return PIPELINES[model_name]

    repo = MODELS[model_name]["repo_id"]
    tokenizer = get_tokenizer(repo)

    # Try AWQ first if available (Transformers fallback path)
    if AWQ_AVAILABLE:
        try:
            print(f"🔄 Loading {repo} with Transformers + AutoAWQ (fallback path)...")
            pipe = load_awq_pipeline(repo, tokenizer)
            PIPELINES[model_name] = pipe
            _schedule_background_warm(model_name)
            # Warm kernels immediately after loading
            Thread(target=lambda: _warm_kernels(model_name), daemon=True).start()
            print(f"✅ Transformers + AutoAWQ pipeline loaded: {model_name}")
            return pipe
        except Exception as exc:
            print(f"⚠️ AutoAWQ load failed for {repo}: {exc}")
            print(f"   → Falling back to BitsAndBytes 8-bit...")

    # Fallback to BitsAndBytes 8-bit
    if BITSANDBYTES_AVAILABLE:
        try:
            print(f"🔄 Loading {repo} with BitsAndBytes 8-bit quantization...")
            quant_config = BitsAndBytesConfig(load_in_8bit=True)
            model_kwargs = {"quantization_config": quant_config}
            if FLASH_ATTN_AVAILABLE:
                model_kwargs["attn_implementation"] = "flash_attention_2"
            
            pipe = pipeline(
                task="text-generation",
                model=repo,
                tokenizer=tokenizer,
                trust_remote_code=True,
                device_map="auto",
                model_kwargs=model_kwargs,
                use_cache=True,
                token=HF_TOKEN,
                torch_dtype=torch.bfloat16,
            )
            
            pipe.model.eval()
            
            # Apply torch.compile for kernel fusion (~10-20% speedup after first call)
            try:
                if hasattr(torch, 'compile'):
                    pipe.model = torch.compile(pipe.model, mode="reduce-overhead")
            except Exception:
                pass
            
            PIPELINES[model_name] = pipe
            _schedule_background_warm(model_name)
            print(f"✅ BitsAndBytes 8-bit pipeline loaded: {model_name}")
            return pipe
        except Exception as exc:
            print(f"⚠️ BitsAndBytes 8-bit load failed for {repo}: {exc}")
            print(f"   → Falling back to FP16/FP32...")

    # Fallback to bfloat16/fp16/fp32 (unquantized)
    for dtype in (torch.bfloat16, torch.float16, torch.float32):
        dtype_name = {torch.bfloat16: "bfloat16", torch.float16: "float16", torch.float32: "float32"}[dtype]
        try:
            print(f"🔄 Loading {repo} with {dtype_name} precision...")
            model_kwargs = {}
            if FLASH_ATTN_AVAILABLE:
                model_kwargs["attn_implementation"] = "flash_attention_2"
            
            pipe = pipeline(
                task="text-generation",
                model=repo,
                tokenizer=tokenizer,
                trust_remote_code=True,
                device_map="auto",
                dtype=dtype,
                model_kwargs=model_kwargs,
                use_cache=True,
                token=HF_TOKEN,
            )
            pipe.model.eval()
            
            # Apply torch.compile for kernel fusion
            try:
                if hasattr(torch, 'compile'):
                    pipe.model = torch.compile(pipe.model, mode="reduce-overhead")
            except Exception:
                pass
            
            PIPELINES[model_name] = pipe
            _schedule_background_warm(model_name)
            print(f"✅ {dtype_name} pipeline loaded: {model_name}")
            return pipe
        except Exception as exc:
            print(f"⚠️ {dtype_name} load failed: {exc}")
            continue

    # Final fallback (no quantization, no FlashAttention)
    print(f"⚠️ All quantization methods failed, using basic pipeline...")
    model_kwargs = {}
    if FLASH_ATTN_AVAILABLE:
        model_kwargs["attn_implementation"] = "flash_attention_2"
    
    pipe = pipeline(
        task="text-generation",
        model=repo,
        tokenizer=tokenizer,
        trust_remote_code=True,
        device_map="auto",
        model_kwargs=model_kwargs,
        use_cache=True,
        token=HF_TOKEN,
    )
    pipe.model.eval()
    
    # Apply torch.compile for kernel fusion
    try:
        if hasattr(torch, 'compile'):
            pipe.model = torch.compile(pipe.model, mode="reduce-overhead")
    except Exception:
        pass
    
    PIPELINES[model_name] = pipe
    _schedule_background_warm(model_name)
    print(f"✅ Basic pipeline loaded: {model_name}")
    return pipe


def _warm_kernels(model_name: str) -> None:
    """Warm up CUDA kernels with a small dummy generation."""
    try:
        # Check if using vLLM
        if VLLM_AVAILABLE and model_name in VLLM_MODELS:
            llm = VLLM_MODELS[model_name]
            # vLLM handles warmup internally, but we can trigger a small generation
            sampling_params = SamplingParams(temperature=0.0, max_tokens=2)
            _ = llm.generate("test", sampling_params)
            print(f"vLLM kernels warmed for {model_name}")
            return
        
        # Transformers pipeline warmup
        pipe = PIPELINES.get(model_name)
        if pipe is None:
            return
        
        tokenizer = pipe.tokenizer
        # Create a minimal prompt for warmup
        warmup_text = "test"
        inputs = tokenizer(warmup_text, return_tensors="pt")
        if hasattr(pipe.model, 'device'):
            inputs = {k: v.to(pipe.model.device) for k, v in inputs.items()}
        elif torch.cuda.is_available():
            inputs = {k: v.cuda() for k, v in inputs.items()}
        
        # Run a tiny generation to JIT-fuse kernels
        with torch.inference_mode():
            _ = pipe.model.generate(
                **inputs,
                max_new_tokens=2,
                do_sample=False,
                use_cache=True,
            )
        print(f"Transformers kernels warmed for {model_name}")
    except Exception as exc:
        print(f"Kernel warmup failed for {model_name}: {exc}")


def _schedule_background_warm(loaded_model: str) -> None:
    global WARMED_REMAINING
    if WARMED_REMAINING:
        return
    warm_remaining = os.environ.get("ROUTER_WARM_REMAINING", "1")
    if warm_remaining not in {"1", "true", "True"}:
        return

    # Check both PIPELINES and VLLM_MODELS for remaining models
    loaded_models = set(PIPELINES.keys()) | set(VLLM_MODELS.keys())
    remaining = [name for name in MODELS if name not in loaded_models]
    if not remaining:
        WARMED_REMAINING = True
        return

    def _warm_all():
        for name in remaining:
            try:
                print(f"Background warm start for {name}")
                load_pipeline(name)
                # Warm kernels after loading
                _warm_kernels(name)
            except Exception as exc:  # pragma: no cover
                print(f"Warm start failed for {name}: {exc}")
        WARMED_REMAINING = True

    Thread(target=_warm_all, daemon=True).start()


def build_router_prompt(
    user_task: str,
    context: str,
    acceptance: str,
    extra_guidance: str,
    difficulty: str,
    tags: str,
) -> str:
    prompt_parts = [ROUTER_SYSTEM_PROMPT.strip(), "\n### Router Inputs\n"]
    prompt_parts.append(f"Difficulty: {difficulty or 'intermediate'}")
    prompt_parts.append(f"Tags: {tags or 'general'}")
    if acceptance.strip():
        prompt_parts.append(f"Acceptance criteria: {acceptance.strip()}")
    if extra_guidance.strip():
        prompt_parts.append(f"Additional guidance: {extra_guidance.strip()}")
    if context.strip():
        prompt_parts.append("\n### Supporting context\n" + context.strip())
    prompt_parts.append("\n### User task\n" + user_task.strip())
    prompt_parts.append("\nReturn only JSON.")
    return "\n".join(prompt_parts)


def extract_json_from_text(text: str) -> str:
    start = text.find("{")
    if start == -1:
        raise ValueError("Router output did not contain a JSON object.")
    depth = 0
    in_string = False
    escape = False
    for idx in range(start, len(text)):
        ch = text[idx]
        if in_string:
            if escape:
                escape = False
            elif ch == "\\":
                escape = True
            elif ch == '"':
                in_string = False
            continue
        if ch == '"':
            in_string = True
            continue
        if ch == '{':
            depth += 1
        elif ch == '}':
            depth -= 1
            if depth == 0:
                return text[start : idx + 1]
    raise ValueError("Router output JSON appears truncated.")


def trim_at_stop_sequences(text: str) -> Tuple[str, bool]:
    """Trim text at stop sequences and return trimmed text and whether a stop was found."""
    earliest = None
    for stop in STOP_SEQUENCES:
        idx = text.find(stop)
        if idx != -1 and (earliest is None or idx < earliest):
            earliest = idx
    if earliest is not None:
        return text[:earliest], True
    return text, False


def is_function_call(text: str) -> bool:
    return bool(TOOL_PATTERN.match(text.strip()))


def validate_router_plan(plan: Dict[str, Any]) -> Tuple[bool, List[str]]:
    issues: List[str] = []
    for key in REQUIRED_KEYS:
        if key not in plan:
            issues.append(f"Missing key: {key}")

    route_plan = plan.get("route_plan")
    if isinstance(route_plan, str) and is_function_call(route_plan):
        plan["route_plan"] = [route_plan]
        route_plan = plan["route_plan"]
    if not isinstance(route_plan, list) or not route_plan:
        issues.append("route_plan must be a non-empty list of tool calls")
    else:
        cleaned: List[str] = []
        for entry in route_plan:
            if isinstance(entry, str) and is_function_call(entry.strip().strip("'\"")):
                cleaned.append(entry.strip().strip("'\""))
            else:
                issues.append(f"route_plan entry is not a tool call: {entry}")
        if cleaned:
            plan["route_plan"] = cleaned

    metrics = plan.get("metrics")
    if not isinstance(metrics, dict):
        issues.append("metrics must be an object containing primary/secondary entries")
    todo = plan.get("todo_list")
    if not isinstance(todo, list) or not todo:
        issues.append("todo_list must contain at least one checklist item")
    else:
        cleaned_todo: List[str] = []
        for entry in todo:
            if isinstance(entry, str):
                text = entry.strip()
                if not text.startswith("- ["):
                    text = text.lstrip("- ")
                    text = f"- [ ] {text}"
                cleaned_todo.append(text)
            else:
                issues.append("todo_list entry must be a string")
        if cleaned_todo:
            plan["todo_list"] = cleaned_todo

    return len(issues) == 0, issues


def format_validation_message(ok: bool, issues: List[str]) -> str:
    if ok:
        return "✅ Router plan includes all required fields."
    bullets = "\n".join(f"- {issue}" for issue in issues)
    return f"❌ Issues detected:\n{bullets}"


def _generate_router_plan_streaming_internal(
    user_task: str,
    context: str,
    acceptance: str,
    extra_guidance: str,
    difficulty: str,
    tags: str,
    model_choice: str,
    max_new_tokens: int,
    temperature: float,
    top_p: float,
    gpu_duration: int,
):
    """Internal generator function for streaming token output."""
    if not user_task.strip():
        yield "", {}, "❌ User task is required.", ""
        return
    
    if model_choice not in MODELS:
        yield "", {}, f"❌ Invalid model choice: {model_choice}. Available: {list(MODELS.keys())}", ""
        return

    try:
        prompt = build_router_prompt(
            user_task=user_task,
            context=context,
            acceptance=acceptance,
            extra_guidance=extra_guidance,
            difficulty=difficulty,
            tags=tags,
        )

        generator = load_pipeline(model_choice)
        
        # Check if using vLLM or Transformers
        is_vllm = VLLM_AVAILABLE and isinstance(generator, LLM)
        
        if is_vllm:
            # Use vLLM streaming API with continuous batching
            sampling_params = SamplingParams(
                temperature=temperature,
                top_p=top_p,
                max_tokens=max_new_tokens,
                stop=STOP_SEQUENCES,
            )
            
            # vLLM streaming generation (non-blocking, continuous batching)
            completion = ""
            parsed_plan: Dict[str, Any] | None = None
            validation_msg = "🔄 Generating..."
            
            # vLLM's generate with stream=True returns RequestOutput iterator
            # Each RequestOutput contains incremental text updates
            stream = generator.generate(prompt, sampling_params, stream=True)
            
            prev_text_len = 0
            for request_output in stream:
                if not request_output.outputs:
                    continue
                
                # Get the latest output (vLLM provides incremental updates)
                output = request_output.outputs[0]
                current_text = output.text
                
                # Extract only new tokens since last update
                if len(current_text) > prev_text_len:
                    new_text = current_text[prev_text_len:]
                    completion += new_text
                    prev_text_len = len(current_text)
                    
                    chunk = completion
                    finished = False
                    display_plan = parsed_plan or {}

                    chunk, finished = trim_at_stop_sequences(chunk)

                    try:
                        json_block = extract_json_from_text(chunk)
                        candidate_plan = json.loads(json_block)
                        ok, issues = validate_router_plan(candidate_plan)
                        validation_msg = format_validation_message(ok, issues)
                        parsed_plan = candidate_plan if ok else parsed_plan
                        display_plan = candidate_plan
                    except Exception:
                        # Ignore until JSON is complete
                        pass

                    yield chunk, display_plan, validation_msg, prompt

                    if finished:
                        completion = chunk
                        break
                
                # Check if generation is finished
                if request_output.finished:
                    break
        else:
            # Use Transformers pipeline (fallback)
            # Get the underlying model and tokenizer
            model = generator.model
            tokenizer = generator.tokenizer
            
            # Set up streaming
            streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
            
            # Prepare inputs
            inputs = tokenizer(prompt, return_tensors="pt")
            if hasattr(model, 'device'):
                inputs = {k: v.to(model.device) for k, v in inputs.items()}
            elif torch.cuda.is_available():
                inputs = {k: v.cuda() for k, v in inputs.items()}
            
            # Start generation in a separate thread
            generation_kwargs = {
                **inputs,
                "max_new_tokens": max_new_tokens,
                "temperature": temperature,
                "top_p": top_p,
                "do_sample": True,
                "streamer": streamer,
                "eos_token_id": tokenizer.eos_token_id,
                "pad_token_id": tokenizer.pad_token_id or tokenizer.eos_token_id,
            }

            def _generate():
                with torch.inference_mode():
                    model.generate(**generation_kwargs)

            thread = Thread(target=_generate)
            thread.start()

        # Stream tokens
            completion = ""
            parsed_plan: Dict[str, Any] | None = None
            validation_msg = "🔄 Generating..."

            for new_text in streamer:
                completion += new_text
                chunk = completion
                finished = False
                display_plan = parsed_plan or {}

                chunk, finished = trim_at_stop_sequences(chunk)

                try:
                    json_block = extract_json_from_text(chunk)
                    candidate_plan = json.loads(json_block)
                    ok, issues = validate_router_plan(candidate_plan)
                    validation_msg = format_validation_message(ok, issues)
                    parsed_plan = candidate_plan if ok else parsed_plan
                    display_plan = candidate_plan
                except Exception:
                    # Ignore until JSON is complete
                    pass

                yield chunk, display_plan, validation_msg, prompt

                if finished:
                    completion = chunk
                break
            
            # Final processing after streaming completes
            thread.join()

        completion = trim_at_stop_sequences(completion.strip())[0]
        if parsed_plan is None:
            try:
                json_block = extract_json_from_text(completion)
                parsed_plan = json.loads(json_block)
                ok, issues = validate_router_plan(parsed_plan)
                validation_msg = format_validation_message(ok, issues)
            except Exception as exc:
                parsed_plan = {}
                validation_msg = f"❌ JSON parsing failed: {exc}"

        yield completion, parsed_plan, validation_msg, prompt
        
    except Exception as exc:
        error_msg = f"❌ Generation failed: {str(exc)}"
        yield "", {}, error_msg, ""


# Pre-create GPU wrappers for common durations at module load time
# This ensures spaces.GPU decorators are detected during startup
_GPU_WRAPPERS: Dict[int, Any] = {}

# Create wrappers for durations: 60, 120, 180, 240, 300, 360, 420, 480, 540, 600, 
# 720, 840, 960, 1080, 1200, 1320, 1440, 1560, 1680, 1800 (every 60s from 60 to 1800)
def _make_gpu_wrapper(duration: int):
    """Factory function to create GPU-decorated wrapper with closure over duration."""
    @spaces.GPU(duration=duration)
    def wrapper(
        user_task: str,
        context: str,
        acceptance: str,
        extra_guidance: str,
        difficulty: str,
        tags: str,
        model_choice: str,
        max_new_tokens: int,
        temperature: float,
        top_p: float,
        gpu_duration: int,
    ):
        yield from _generate_router_plan_streaming_internal(
            user_task, context, acceptance, extra_guidance,
            difficulty, tags, model_choice, max_new_tokens,
            temperature, top_p, duration
        )
    return wrapper

# Pre-create all wrappers at module load time
for duration in range(60, 1801, 60):
    _GPU_WRAPPERS[duration] = _make_gpu_wrapper(duration)


def generate_router_plan_streaming(
    user_task: str,
    context: str,
    acceptance: str,
    extra_guidance: str,
    difficulty: str,
    tags: str,
    model_choice: str,
    max_new_tokens: int,
    temperature: float,
    top_p: float,
    gpu_duration: int = 600,
):
    """
    Generate router plan with streaming output.
    
    Uses user-specified gpu_duration to select the appropriate GPU wrapper.
    """
    # Round to nearest 60 seconds and clamp between 60 and 1800
    rounded_duration = ((gpu_duration + 30) // 60) * 60
    rounded_duration = max(60, min(1800, rounded_duration))
    
    # Get the pre-created wrapper with this duration
    wrapper = _GPU_WRAPPERS[rounded_duration]
    yield from wrapper(
        user_task, context, acceptance, extra_guidance,
        difficulty, tags, model_choice, max_new_tokens,
        temperature, top_p, rounded_duration
    )


def clear_outputs():
    return "", {}, "Awaiting generation.", ""


def build_ui():
    description = "Use the CourseGPT-Pro router checkpoints (Gemma3/Qwen3) hosted on ZeroGPU to generate structured routing plans."
    with gr.Blocks(theme=gr.themes.Soft(), css="""
        textarea { font-family: 'JetBrains Mono', 'Fira Code', monospace; }
        .status-ok { color: #0d9488; font-weight: 600; }
        .status-bad { color: #dc2626; font-weight: 600; }
    """) as demo:
        gr.Markdown("# 🛰️ Router Control Room — ZeroGPU" )
        gr.Markdown(description)
        
        with gr.Row():
            with gr.Column(scale=3):
                user_task = gr.Textbox(
                    label="User Task / Problem Statement",
                    placeholder="Describe the homework-style query that needs routing...",
                    lines=8,
                    value="Explain how to solve a constrained optimization homework problem that mixes calculus and coding steps.",
                )
                context = gr.Textbox(
                    label="Supporting Context (optional)",
                    placeholder="Paste any retrieved evidence, PDFs, or rubric notes.",
                    lines=4,
                )
                acceptance = gr.Textbox(
                    label="Acceptance Criteria",
                    placeholder="Bullet list of 'definition of done' checks.",
                    lines=3,
                    value="- Provide citations for every claim.\n- Ensure /math verifies /code output.",
                )
                extra_guidance = gr.Textbox(
                    label="Additional Guidance",
                    placeholder="Special constraints, tools to avoid, etc.",
                    lines=3,
                )
            with gr.Column(scale=2):
                model_choice = gr.Dropdown(
                    label="Router Checkpoint",
                    choices=list(MODELS.keys()),
                    value=list(MODELS.keys())[0] if MODELS else None,
                    allow_custom_value=False,
                )
                difficulty = gr.Radio(
                    label="Difficulty Tier",
                    choices=["introductory", "intermediate", "advanced"],
                    value="advanced",
                    interactive=True,
                )
                tags = gr.Textbox(
                    label="Tags",
                    placeholder="Comma-separated e.g. calculus, optimization, python",
                    value="calculus, optimization, python",
                )
                max_new_tokens = gr.Slider(256, 20000, value=16000, step=32, label="Max New Tokens")
                temperature = gr.Slider(0.0, 1.5, value=0.2, step=0.05, label="Temperature")
                top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p")
                gpu_duration = gr.Slider(60, 1800, value=600, step=60, label="GPU Duration (seconds)", info="Maximum GPU time allocation for this request")

        with gr.Row():
            generate_btn = gr.Button("Generate Router Plan", variant="primary", scale=1)
            clear_btn = gr.Button("Clear", variant="secondary", scale=1)

        with gr.Row():
            raw_output = gr.Textbox(label="Raw Model Output", lines=12)
            plan_json = gr.JSON(label="Parsed Router Plan")
        validation_msg = gr.Markdown("Awaiting generation.")
        prompt_view = gr.Textbox(label="Full Prompt", lines=10)

        generate_btn.click(
            generate_router_plan_streaming,
            inputs=[
                user_task,
                context,
                acceptance,
                extra_guidance,
                difficulty,
                tags,
                model_choice,
                max_new_tokens,
                temperature,
                top_p,
                gpu_duration,
            ],
            outputs=[raw_output, plan_json, validation_msg, prompt_view],
            show_progress="full",
            api_name="/generate_router_plan_streaming",
        )

        clear_btn.click(
            fn=clear_outputs,
            outputs=[raw_output, plan_json, validation_msg, prompt_view],
            api_name="/clear_outputs",
        )

    return demo



def _prefetch_from_env() -> None:
    entries = os.environ.get("ROUTER_PREFETCH_MODELS")
    if entries:
        names = [item.strip() for item in entries.split(",") if item.strip()]
    else:
        single = os.environ.get("ROUTER_PREFETCH_MODEL")
        names = [single] if single else []

    if names == ["ALL"] or names == ["all"]:
        names = list(MODELS.keys())

    for name in names:
        if name not in MODELS:
            print(f"Prefetch skipped, unknown model: {name}")
            continue
        try:
            load_pipeline(name)
            print(f"Prefetched router model: {name}")
        except Exception as exc:  # pragma: no cover
            print(f"Prefetch failed for {name}: {exc}")


_prefetch_from_env()

demo = build_ui()

if __name__ == "__main__":  # pragma: no cover
    # Support both Hugging Face Spaces and Google Cloud Run
    # Cloud Run uses PORT, Hugging Face Spaces uses GRADIO_SERVER_PORT
    port = int(os.environ.get("PORT", os.environ.get("GRADIO_SERVER_PORT", 7860)))
    server_name = os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0")
    
    demo.launch(
        server_name=server_name,
        server_port=port,
        show_api=True
    )