import os
import time
import gc
import threading
from itertools import islice
from datetime import datetime
import re  # for parsing <think> blocks
import gradio as gr
import torch
from transformers import pipeline, TextIteratorStreamer
from transformers import AutoTokenizer
from duckduckgo_search import DDGS
import spaces  # Import spaces early to enable ZeroGPU support

# Optional: Disable GPU visibility if you wish to force CPU usage
# os.environ["CUDA_VISIBLE_DEVICES"] = ""

# ------------------------------
# Global Cancellation Event
# ------------------------------
cancel_event = threading.Event()

# ------------------------------
# Torch-Compatible Model Definitions with Adjusted Descriptions
# ------------------------------
MODELS = {
    "Taiwan-ELM-1_1B-Instruct": {"repo_id": "liswei/Taiwan-ELM-1_1B-Instruct", "description": "Taiwan-ELM-1_1B-Instruct"},
    "Taiwan-ELM-270M-Instruct": {"repo_id": "liswei/Taiwan-ELM-270M-Instruct", "description": "Taiwan-ELM-270M-Instruct"},
    # "Granite-4.0-Tiny-Preview": {"repo_id": "ibm-granite/granite-4.0-tiny-preview", "description": "Granite-4.0-Tiny-Preview"},
    "Qwen3-0.6B":    {"repo_id":"Qwen/Qwen3-0.6B","description":"Dense causal language model with 0.6 B total parameters (0.44 B non-embedding), 28 transformer layers, 16 query heads & 8 KV heads, native 32 768-token context window, dual-mode generation, full multilingual & agentic capabilities."},
    "Qwen3-1.7B":    {"repo_id":"Qwen/Qwen3-1.7B","description":"Dense causal language model with 1.7 B total parameters (1.4 B non-embedding), 28 layers, 16 query heads & 8 KV heads, 32 768-token context, stronger reasoning vs. 0.6 B variant, dual-mode inference, instruction following across 100+ languages."},
    "Qwen3-4B":      {"repo_id":"Qwen/Qwen3-4B","description":"Dense causal language model with 4.0 B total parameters (3.6 B non-embedding), 36 layers, 32 query heads & 8 KV heads, native 32 768-token context (extendable to 131 072 via YaRN), balanced mid-range capacity & long-context reasoning."},
    "Qwen3-8B":      {"repo_id":"Qwen/Qwen3-8B","description":"Dense causal language model with 8.2 B total parameters (6.95 B non-embedding), 36 layers, 32 query heads & 8 KV heads, 32 768-token context (131 072 via YaRN), excels at multilingual instruction following & zero-shot tasks."},
    "Qwen3-14B":     {"repo_id":"Qwen/Qwen3-14B","description":"Dense causal language model with 14.8 B total parameters (13.2 B non-embedding), 40 layers, 40 query heads & 8 KV heads, 32 768-token context (131 072 via YaRN), enhanced human preference alignment & advanced agent integration."},
    # "Qwen3-32B":     {"repo_id":"Qwen/Qwen3-32B","description":"Dense causal language model with 32.8 B total parameters (31.2 B non-embedding), 64 layers, 64 query heads & 8 KV heads, 32 768-token context (131 072 via YaRN), flagship variant delivering state-of-the-art reasoning & instruction following."},
    # "Qwen3-30B-A3B": {"repo_id":"Qwen/Qwen3-30B-A3B","description":"Mixture-of-Experts model with 30.5 B total parameters (29.9 B non-embedding, 3.3 B activated per token), 48 layers, 128 experts (8 activated per token), 32 query heads & 4 KV heads, 32 768-token context (131 072 via YaRN), MoE routing for scalable specialized reasoning."},
    # "Qwen3-235B-A22B":{"repo_id":"Qwen/Qwen3-235B-A22B","description":"Mixture-of-Experts model with 235 B total parameters (234 B non-embedding, 22 B activated per token), 94 layers, 128 experts (8 activated per token), 64 query heads & 4 KV heads, 32 768-token context (131 072 via YaRN), ultra-scale reasoning & agentic workflows."},
    "Gemma-3-4B-IT": {"repo_id": "unsloth/gemma-3-4b-it", "description": "Gemma-3-4B-IT"},
    "SmolLM2-135M-Instruct-TaiwanChat": {"repo_id": "Luigi/SmolLM2-135M-Instruct-TaiwanChat", "description": "SmolLM2‑135M Instruct fine-tuned on TaiwanChat"},
    "SmolLM2-135M-Instruct": {"repo_id": "HuggingFaceTB/SmolLM2-135M-Instruct", "description": "Original SmolLM2‑135M Instruct"},
    "SmolLM2-360M-Instruct-TaiwanChat": {"repo_id": "Luigi/SmolLM2-360M-Instruct-TaiwanChat", "description": "SmolLM2‑360M Instruct fine-tuned on TaiwanChat"},
    "Llama-3.2-Taiwan-3B-Instruct": {"repo_id": "lianghsun/Llama-3.2-Taiwan-3B-Instruct", "description": "Llama-3.2-Taiwan-3B-Instruct"},
    "MiniCPM3-4B": {"repo_id": "openbmb/MiniCPM3-4B", "description": "MiniCPM3-4B"},
    "Qwen2.5-3B-Instruct": {"repo_id": "Qwen/Qwen2.5-3B-Instruct", "description": "Qwen2.5-3B-Instruct"},
    "Qwen2.5-7B-Instruct": {"repo_id": "Qwen/Qwen2.5-7B-Instruct", "description": "Qwen2.5-7B-Instruct"},
    "Phi-4-mini-Reasoning": {"repo_id": "microsoft/Phi-4-mini-reasoning", "description": "Phi-4-mini-Reasoning"},
    # "Phi-4-Reasoning":      {"repo_id": "microsoft/Phi-4-reasoning",      "description": "Phi-4-Reasoning"},
    "Phi-4-mini-Instruct": {"repo_id": "microsoft/Phi-4-mini-instruct", "description": "Phi-4-mini-Instruct"},
    "Meta-Llama-3.1-8B-Instruct": {"repo_id": "MaziyarPanahi/Meta-Llama-3.1-8B-Instruct", "description": "Meta-Llama-3.1-8B-Instruct"},
    "DeepSeek-R1-Distill-Llama-8B": {"repo_id": "unsloth/DeepSeek-R1-Distill-Llama-8B", "description": "DeepSeek-R1-Distill-Llama-8B"},
    "Mistral-7B-Instruct-v0.3": {"repo_id": "MaziyarPanahi/Mistral-7B-Instruct-v0.3", "description": "Mistral-7B-Instruct-v0.3"},
    "Qwen2.5-Coder-7B-Instruct": {"repo_id": "Qwen/Qwen2.5-Coder-7B-Instruct", "description": "Qwen2.5-Coder-7B-Instruct"},
    "Qwen2.5-Omni-3B":   {"repo_id": "Qwen/Qwen2.5-Omni-3B",   "description": "Qwen2.5-Omni-3B"},
    "MiMo-7B-RL":        {"repo_id": "XiaomiMiMo/MiMo-7B-RL",   "description": "MiMo-7B-RL"},

}

# Global cache for pipelines to avoid re-loading.
PIPELINES = {}

def load_pipeline(model_name):
    """
    Load and cache a transformers pipeline for text generation.
    Tries bfloat16, falls back to float16 or float32 if unsupported.
    """
    global PIPELINES
    if model_name in PIPELINES:
        return PIPELINES[model_name]
    repo = MODELS[model_name]["repo_id"]
    tokenizer = AutoTokenizer.from_pretrained(repo)
    for dtype in (torch.bfloat16, torch.float16, torch.float32):
        try:
            pipe = pipeline(
                task="text-generation",
                model=repo,
                tokenizer=tokenizer,
                trust_remote_code=True,
                torch_dtype=dtype,
                device_map="auto"
            )
            PIPELINES[model_name] = pipe
            return pipe
        except Exception:
            continue
    # Final fallback
    pipe = pipeline(
        task="text-generation",
        model=repo,
        tokenizer=tokenizer,
        trust_remote_code=True,
        device_map="auto"
    )
    PIPELINES[model_name] = pipe
    return pipe


def retrieve_context(query, max_results=6, max_chars=600):
    """
    Retrieve search snippets from DuckDuckGo (runs in background).
    Returns a list of result strings.
    """
    try:
        with DDGS() as ddgs:
            return [f"{i+1}. {r.get('title','No Title')} - {r.get('body','')[:max_chars]}"
                    for i, r in enumerate(islice(ddgs.text(query, region="wt-wt", safesearch="off", timelimit="y"), max_results))]
    except Exception:
        return []

def format_conversation(history, system_prompt, tokenizer):
    if hasattr(tokenizer, "chat_template") and tokenizer.chat_template:
        messages = [{"role": "system", "content": system_prompt.strip()}] + history
        return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
    else:
        # Fallback for base LMs without chat template
        prompt = system_prompt.strip() + "\n"
        for msg in history:
            if msg['role'] == 'user':
                prompt += "User: " + msg['content'].strip() + "\n"
            elif msg['role'] == 'assistant':
                prompt += "Assistant: " + msg['content'].strip() + "\n"
        if not prompt.strip().endswith("Assistant:"):
            prompt += "Assistant: "
        return prompt

@spaces.GPU(duration=60)
def chat_response(user_msg, chat_history, system_prompt,
                  enable_search, max_results, max_chars,
                  model_name, max_tokens, temperature,
                  top_k, top_p, repeat_penalty, search_timeout):
    """
    Generates streaming chat responses, optionally with background web search.
    """
    cancel_event.clear()
    history = list(chat_history or [])
    history.append({'role': 'user', 'content': user_msg})

    # Launch web search if enabled
    debug = ''
    search_results = []
    if enable_search:
        debug = 'Search task started.'
        thread_search = threading.Thread(
            target=lambda: search_results.extend(
                retrieve_context(user_msg, int(max_results), int(max_chars))
            )
        )
        thread_search.daemon = True
        thread_search.start()
    else:
        debug = 'Web search disabled.'

    try:

        # merge any fetched search results into the system prompt
        if search_results:
            enriched = system_prompt.strip() + "\n\nRelevant context:\n" + "\n".join(search_results)
        else:
            enriched = system_prompt

        # wait up to 1s for snippets, then replace debug with them
        if enable_search:
            thread_search.join(timeout=float(search_timeout))
            if search_results:
                debug = "### Search results merged into prompt\n\n" + "\n".join(
                    f"- {r}" for r in search_results
                )
            else:
                debug = "*No web search results found.*"

        # merge fetched snippets into the system prompt
        if search_results:
            enriched = system_prompt.strip() + "\n\nRelevant context:\n" + "\n".join(search_results)
        else:
            enriched = system_prompt

        pipe = load_pipeline(model_name)
        prompt = format_conversation(history, enriched, pipe.tokenizer)
        prompt_debug = f"\n\n--- Prompt Preview ---\n```\n{prompt}\n```"
        streamer = TextIteratorStreamer(pipe.tokenizer,
                                        skip_prompt=True,
                                        skip_special_tokens=True)
        gen_thread = threading.Thread(
            target=pipe,
            args=(prompt,),
            kwargs={
                'max_new_tokens': max_tokens,
                'temperature': temperature,
                'top_k': top_k,
                'top_p': top_p,
                'repetition_penalty': repeat_penalty,
                'streamer': streamer,
                'return_full_text': False,
            }
        )
        gen_thread.start()

        # Buffers for thought vs answer
        thought_buf = ''
        answer_buf = ''
        in_thought = False

        # Stream tokens
        for chunk in streamer:
            if cancel_event.is_set():
                break
            text = chunk

            # Detect start of thinking
            if not in_thought and '<think>' in text:
                in_thought = True
                # Insert thought placeholder
                history.append({
                    'role': 'assistant',
                    'content': '',
                    'metadata': {'title': '💭 Thought'}
                })
                # Capture after opening tag
                after = text.split('<think>', 1)[1]
                thought_buf += after
                # If closing tag in same chunk
                if '</think>' in thought_buf:
                    before, after2 = thought_buf.split('</think>', 1)
                    history[-1]['content'] = before.strip()
                    in_thought = False
                    # Start answer buffer
                    answer_buf = after2
                    history.append({'role': 'assistant', 'content': answer_buf})
                else:
                    history[-1]['content'] = thought_buf
                yield history, debug
                continue

            # Continue thought streaming
            if in_thought:
                thought_buf += text
                if '</think>' in thought_buf:
                    before, after2 = thought_buf.split('</think>', 1)
                    history[-1]['content'] = before.strip()
                    in_thought = False
                    # Start answer buffer
                    answer_buf = after2
                    history.append({'role': 'assistant', 'content': answer_buf})
                else:
                    history[-1]['content'] = thought_buf
                yield history, debug
                continue

            # Stream answer
            if not answer_buf:
                history.append({'role': 'assistant', 'content': ''})
            answer_buf += text
            history[-1]['content'] = answer_buf
            yield history, debug

        gen_thread.join()
        yield history, debug + prompt_debug
    except Exception as e:
        history.append({'role': 'assistant', 'content': f"Error: {e}"})
        yield history, debug
    finally:
        gc.collect()


def cancel_generation():
    cancel_event.set()
    return 'Generation cancelled.'


def update_default_prompt(enable_search):
    today = datetime.now().strftime('%Y-%m-%d')
    return f"You are a helpful assistant. Today is {today}."

# ------------------------------
# Gradio UI
# ------------------------------
with gr.Blocks(title="LLM Inference with ZeroGPU") as demo:
    gr.Markdown("## 🧠 ZeroGPU LLM Inference with Web Search")
    gr.Markdown("Interact with the model. Select parameters and chat below.")
    with gr.Row():
        with gr.Column(scale=3):
            model_dd = gr.Dropdown(label="Select Model", choices=list(MODELS.keys()), value=list(MODELS.keys())[0])
            search_chk = gr.Checkbox(label="Enable Web Search", value=True)
            sys_prompt = gr.Textbox(label="System Prompt", lines=3, value=update_default_prompt(search_chk.value))
            gr.Markdown("### Generation Parameters")
            max_tok = gr.Slider(64, 16384, value=2048, step=32, label="Max Tokens")
            temp = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
            k = gr.Slider(1, 100, value=40, step=1, label="Top-K")
            p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-P")
            rp = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repetition Penalty")
            gr.Markdown("### Web Search Settings")
            mr = gr.Number(value=6, precision=0, label="Max Results")
            mc = gr.Number(value=600, precision=0, label="Max Chars/Result")
            st = gr.Slider(minimum=0.0, maximum=30.0, step=0.5, value=5.0, label="Search Timeout (s)")
            clr = gr.Button("Clear Chat")
            cnl = gr.Button("Cancel Generation")
        with gr.Column(scale=7):
            chat = gr.Chatbot(type="messages")
            txt = gr.Textbox(placeholder="Type your message and press Enter...")
            dbg = gr.Markdown()

    search_chk.change(fn=update_default_prompt, inputs=search_chk, outputs=sys_prompt)
    clr.click(fn=lambda: ([], "", ""), outputs=[chat, txt, dbg])
    cnl.click(fn=cancel_generation, outputs=dbg)
    txt.submit(fn=chat_response,
               inputs=[txt, chat, sys_prompt, search_chk, mr, mc,
                       model_dd, max_tok, temp, k, p, rp, st],
               outputs=[chat, dbg])
    demo.launch()