import spaces
from transformers import TextIteratorStreamer, AutoModelForCausalLM, AutoTokenizer
from threading import Thread
import gradio as gr
import re
from openai_harmony import (
    load_harmony_encoding,
    HarmonyEncodingName,
    Role,
    Message,
    Conversation,
    SystemContent,
    DeveloperContent,
    ReasoningEffort,
)

RE_REASONING = re.compile(r'(?i)Reasoning:\s*(low|medium|high)')
RE_FINAL_MARKER = re.compile(r'(?i)assistantfinal')
RE_ANALYSIS_PREFIX = re.compile(r'(?i)^analysis\s*')

def parse_reasoning_and_instructions(system_prompt: str):
    instructions = system_prompt or "You are a helpful assistant."
    match = RE_REASONING.search(instructions)
    effort_key = match.group(1).lower() if match else 'medium'
    effort = {
        'low': ReasoningEffort.LOW,
        'medium': ReasoningEffort.MEDIUM,
        'high': ReasoningEffort.HIGH,
    }.get(effort_key, ReasoningEffort.MEDIUM)
    cleaned_instructions = RE_REASONING.sub('', instructions).strip()
    return effort, cleaned_instructions

model_id = "ArliAI/gpt-oss-20b-Derestricted"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    trust_remote_code=True,
    device_map=None,
)

enc = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)

def format_conversation_history(chat_history):
    """Handle legacy/new format"""
    messages = []
    for item in chat_history:
        if isinstance(item, dict):
            role = item.get("role", "user")
            content = item.get("content", "")
            if isinstance(content, list):
                content = content[0].get("text", str(content)) if content else ""
            messages.append({"role": role, "content": content})
        elif isinstance(item, (list, tuple)):
            if item[0]:
                messages.append({"role": "user", "content": item[0]})
            if len(item) > 1 and item[1]:
                messages.append({"role": "assistant", "content": item[1]})
    return messages

@spaces.GPU(duration=120)
def generate_response(input_data, chat_history, max_new_tokens, system_prompt, temperature, top_p, top_k, repetition_penalty):
    model.to('cuda')
    
    new_message = {"role": "user", "content": input_data}
    processed_history = format_conversation_history(chat_history)
    effort, instructions = parse_reasoning_and_instructions(system_prompt)
    system_content = SystemContent.new().with_reasoning_effort(effort)
    developer_content = DeveloperContent.new().with_instructions(instructions)
    harmony_messages = [
        Message.from_role_and_content(Role.SYSTEM, system_content),
        Message.from_role_and_content(Role.DEVELOPER, developer_content),
    ]
    
    for m in processed_history + [new_message]:
        role = Role.USER if m["role"] == "user" else Role.ASSISTANT
        harmony_messages.append(Message.from_role_and_content(role, m["content"]))
    conversation = Conversation.from_messages(harmony_messages)
    prompt_tokens = enc.render_conversation_for_completion(conversation, Role.ASSISTANT)
    prompt_text = tokenizer.decode(prompt_tokens, skip_special_tokens=False)

    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    
    inputs = tokenizer(prompt_text, return_tensors="pt").to('cuda')

    generation_kwargs = {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"],
        "max_new_tokens": max_new_tokens,
        "do_sample": True,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repetition_penalty": repetition_penalty,
        "streamer": streamer,
    }
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    thinking = ""
    final = ""
    started_final = False
    for chunk in streamer:
        if not started_final:
            parts = RE_FINAL_MARKER.split(chunk, maxsplit=1)
            thinking += parts[0]
            if len(parts) > 1:
                final += parts[-1]
                started_final = True
        else:
            final += chunk
        clean_thinking = RE_ANALYSIS_PREFIX.sub('', thinking).strip()
        clean_final = final.strip()
        formatted = f"<details open><summary>Click to view Thinking Process</summary>\n\n{clean_thinking}\n\n</details>\n\n{clean_final}"
        yield formatted
    
    thread.join()

demo = gr.ChatInterface(
    fn=generate_response,
    additional_inputs=[
        gr.Slider(label="Max new tokens", minimum=64, maximum=4096, step=1, value=2048),
        gr.Textbox(
            label="System Prompt",
            value="You are a helpful assistant. Reasoning: medium",
            lines=4,
            placeholder="Change system prompt"
        ),
        gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, step=0.1, value=0.7),
        gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=0.9),
        gr.Slider(label="Top-k", minimum=1, maximum=100, step=1, value=50),
        gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.0)
    ],
    examples=[
        ["Explain Newton's laws clearly and concisely"],
        ["What are the benefits of open weight AI models"],
        ["Write a Python function to calculate the Fibonacci sequence"],
    ],
    cache_examples=False,
    description="""# GPT-OSS 20B Derestricted.""",
    fill_height=True,
    stop_btn="Stop Generation",
)

if __name__ == "__main__":
    demo.launch()