from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import torch
from threading import Thread
import gradio as gr
import spaces
import re
from peft import PeftModel

# Load the base model
try:
    base_model = AutoModelForCausalLM.from_pretrained(
        "openai/gpt-oss-20b",
        torch_dtype="auto",
        device_map="auto",
        attn_implementation="kernels-community/vllm-flash-attention3"
    )
    tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
    
    # Load the LoRA adapter
    try:
        model = PeftModel.from_pretrained(base_model, "Tonic/gpt-oss-20b-multilingual-reasoner")
        print("✅ LoRA model loaded successfully!")
    except Exception as lora_error:
        print(f"⚠️ LoRA adapter failed to load: {lora_error}")
        print("🔄 Falling back to base model...")
        model = base_model
        
except Exception as e:
    print(f"❌ Error loading model: {e}")
    raise e

def format_conversation_history(chat_history):
    messages = []
    for item in chat_history:
        role = item["role"]
        content = item["content"]
        if isinstance(content, list):
            content = content[0]["text"] if content and "text" in content[0] else str(content)
        messages.append({"role": role, "content": content})
    return messages

@spaces.GPU(duration=60)
def generate_response(input_data, chat_history, max_new_tokens, system_prompt, temperature, top_p, top_k, repetition_penalty):
    new_message = {"role": "user", "content": input_data}
    system_message = [{"role": "system", "content": system_prompt}] if system_prompt else []
    processed_history = format_conversation_history(chat_history)
    messages = system_message + processed_history + [new_message]
    
    # Use the model's chat template to format the conversation properly
    # This is crucial for GPT-OSS-20B which expects the Harmony format
    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    # Alternative streaming approach with manual chunking
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate in smaller chunks for better streaming
    chunk_size = 50  # Generate 50 tokens at a time
    full_response = ""
    
    with torch.no_grad():
        for i in range(0, max_new_tokens, chunk_size):
            current_max_tokens = min(chunk_size, max_new_tokens - i)
            
            outputs = model.generate(
                **inputs,
                max_new_tokens=current_max_tokens,
                do_sample=True,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                repetition_penalty=repetition_penalty,
                pad_token_id=tokenizer.eos_token_id,
                use_cache=True
            )
            
            # Decode the new tokens
            new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
            new_text = tokenizer.decode(new_tokens, skip_special_tokens=True)
            
            if new_text:
                full_response += new_text
                
                # Process for thinking/final split
                thinking = ""
                final = ""
                started_final = False
                
                if "assistantfinal" in full_response.lower():
                    split_parts = re.split(r'assistantfinal', full_response, maxsplit=1)
                    thinking = split_parts[0]
                    final = split_parts[1] if len(split_parts) > 1 else ""
                    started_final = True
                else:
                    thinking = full_response
                
                clean_thinking = re.sub(r'^analysis\s*', '', 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
            
            # Update inputs for next iteration
            inputs = {"input_ids": outputs}
            
            # Check for end of generation
            if outputs[0][-1].item() == tokenizer.eos_token_id:
                break

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=[
        [{"text": "Explain Newton laws clearly and concisely"}],
        [{"text": "Write a Python function to calculate the Fibonacci sequence"}],
        [{"text": "What are the benefits of open weight AI models"}],
    ],
    cache_examples=False,
    type="messages",
    description="""
# 🙋🏻‍♂️Welcome to 🌟Tonic's gpt-oss-20b Multilingual Reasoner Demo !
Wait couple of seconds initially. You can adjust reasoning level in the system prompt like "Reasoning: high.
    """,
    fill_height=True,
    textbox=gr.Textbox(
        label="Query Input",
        placeholder="Type your prompt"
    ),
    stop_btn="Stop Generation",
    multimodal=False,
    theme=gr.themes.Soft()
)

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