app.py

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

veri_model_path = "nyu-dice-lab/VeriThoughts-Reasoning-7B"

device = "cuda:0" if torch.cuda.is_available() else "cpu"

# Try loading the model with explicit error handling
try:
    veri_tokenizer = AutoTokenizer.from_pretrained(veri_model_path)

    veri_model = AutoModelForCausalLM.from_pretrained(
        veri_model_path, 
        device_map="auto", 
        torch_dtype="auto",
        trust_remote_code=True,
        use_cache=True,  # Enable KV caching
        attn_implementation="flash_attention_2" if torch.cuda.is_available() else None
    )

except Exception as e:
    print(f"Model loading error: {e}")
    veri_model = None
    veri_tokenizer = None

@spaces.GPU(duration=60)
def generate_response(user_message, history):
    if not veri_model or not veri_tokenizer:
        return history + [["Error", "Model not loaded properly"]]
    
    if not user_message.strip():
        return history
        
    # Simple generation without streaming first
    system_message = "You are VeriThoughts, a helpful assistant that thinks step by step. You are finetuned from a Qwen model, created by Alibaba Cloud. If you are asked a Verilog question, make sure your input and output interface has the same names as described in the question. If you are asked to generate code, please start your Verilog code with CODE BEGIN and end with CODE END."
    
    conversation = f"System: {system_message}\n"
    recent_history = history[-3:] if len(history) > 3 else history
  
    for h in recent_history:
        conversation += f"User: {h[0]}\nAssistant: {h[1]}\n"
    conversation += f"User: {user_message}\nAssistant:"
    
    inputs = veri_tokenizer(
        conversation,                     
        return_tensors="pt", 
        truncation=True, 
        max_length=2048,
        padding=True,
        return_attention_mask=True
    ).to(device)
    
    with torch.no_grad():
        outputs = veri_model.generate(
            **inputs,
            max_new_tokens=4096,
            temperature=0.7,
            top_p=0.95,
            do_sample=True,
            top_k=50,            # Top-k sampling for efficiency
 #           pad_token_id=veri_tokenizer.eos_token_id,
 #           eos_token_id=veri_tokenizer.eos_token_id,
            use_cache=True,      # Enable KV caching for faster generation
            repetition_penalty=1.1,  # Reduce repetition
            length_penalty=1.0,
            early_stopping=True,     # Stop early when appropriate
            num_beams=1,            # Greedy search for speed
            pad_token_id=veri_tokenizer.eos_token_id
        )
    
    response = veri_tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    
    # Return updated history
    return history + [[user_message, response.strip()]]

# Create minimal interface
with gr.Blocks(
    title="VeriThoughts-7B Chatbot",
    css="""
    .gradio-container {
        max-width: 1200px !important;
    }
    .chat-message {
        font-size: 14px;
    }
    """
) as demo:
    gr.Markdown(
        """
        # 🤖 VeriThoughts-7B Chatbot
        
        An AI assistant specialized in Verilog coding and digital design.
        
        **Tips for better results:**
        - Mention input/output port names clearly
        - Ask for step-by-step explanations
        """
    )
    
    chatbot = gr.Chatbot(value=[], label="Chat")
    msg = gr.Textbox(label="Your message", placeholder="Ask me about Verilog design, syntax, or implementation...")
    clear = gr.Button("Clear")
    
    # Simple event handling
    msg.submit(
        fn=generate_response, 
        inputs=[msg, chatbot], 
        outputs=chatbot
    ).then(
        lambda: "", 
        inputs=None, 
        outputs=msg
    )
    
    clear.click(lambda: [], outputs=chatbot)

# Launch without ssr_mode parameter which might cause issues
demo.launch(share=True)