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vibethinker
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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch


class VibeThinker:
    def __init__(self, model_path):
        self.model_path = model_path
        self.model = AutoModelForCausalLM.from_pretrained(
            self.model_path,
            low_cpu_mem_usage=True,
            torch_dtype=torch.bfloat16,
            device_map="auto"
        )
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)

    def infer_text(self, messages):
        text = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )
        model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)

        generation_config = dict(
            max_new_tokens=4096,
            do_sample=True,
            temperature=0.6,
            top_p=0.95,
            top_k=-1
        )
        
        generated_ids = self.model.generate(
            **model_inputs,
            generation_config=GenerationConfig(**generation_config)
        )
        
        generated_ids = [
            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
        ]

        response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
        return response


# Initialize the model
print("Loading VibeThinker model...")
vibe_model = VibeThinker('WeiboAI/VibeThinker-1.5B')
print("Model loaded successfully!")


def respond(message, history):
    """
    Generate response for the chatbot.
    
    Args:
        message: The user's current message
        history: List of previous conversation messages in [user, assistant] format
    """
    # Convert history to messages format
    messages = []
    for user_msg, assistant_msg in history:
        messages.append({"role": "user", "content": user_msg})
        messages.append({"role": "assistant", "content": assistant_msg})
    
    # Add current message
    messages.append({"role": "user", "content": message})
    
    # Generate response
    response = vibe_model.infer_text(messages)
    
    return response


# Create the Gradio interface
with gr.Blocks(
    theme=gr.themes.Soft(),
    css="""
    .header-link { text-decoration: none; color: inherit; }
    .header-link:hover { text-decoration: underline; }
    """
) as demo:
    gr.Markdown(
        """
        # 💭 VibeThinker Chatbot
        Chat with [WeiboAI/VibeThinker-1.5B](https://huggingface.co/WeiboAI/VibeThinker-1.5B) - a powerful conversational AI model.
        
        <a href="https://huggingface.co/spaces/akhaliq/anycoder" class="header-link">Built with anycoder</a>
        """
    )
    
    chatbot = gr.ChatInterface(
        fn=respond,
        type="messages",
        title="",
        description="Ask me anything! I'm powered by VibeThinker.",
        examples=[
            "What is the meaning of life?",
            "Explain quantum computing in simple terms",
            "Write a short poem about artificial intelligence",
            "How can I improve my productivity?",
        ],
        cache_examples=False,
        retry_btn=None,
        undo_btn=None,
        clear_btn="Clear Chat",
    )
    
    gr.Markdown(
        """
        ### About VibeThinker
        VibeThinker is a 1.5B parameter conversational AI model designed for engaging and thoughtful conversations.
        The model uses temperature sampling (0.6) for balanced creativity and coherence.
        """
    )

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