import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM

TARGET_MODEL_ID = "google/gemma-4-E2B-it"
ASSISTANT_MODEL_ID = "google/gemma-4-E2B-it-assistant"

# Target Model
processor = AutoProcessor.from_pretrained(TARGET_MODEL_ID)
target_model = AutoModelForCausalLM.from_pretrained(
    TARGET_MODEL_ID,
    dtype="auto",
    device_map="auto",

)

# Assistant Model (the drafter)
assistant_model = AutoModelForCausalLM.from_pretrained(
    ASSISTANT_MODEL_ID,
    dtype="auto",
    device_map="auto",
)


def greet(name):
    # Prompt
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": name},
    ]
    
    # Process input
    text = processor.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True, 
    )
    inputs = processor(text=text, return_tensors="pt").to(target_model.device)
    input_len = inputs["input_ids"].shape[-1]
    
    # Generate output
    outputs = target_model.generate(
        **inputs,
        assistant_model=assistant_model,
        max_new_tokens=256,
    )
    response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
    
    # Parse output
    textofinal =processor.parse_response(response)
    return textofinal

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()