import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load NVLM-D-72B model and tokenizer
# model_name = "nvidia/NVLM-D-72B"
model_name = "nvidia/NVLM-D-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    trust_remote_code=True, 
    device_map="auto"
)

# Inference function
def generate_response(prompt, max_tokens=50):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")  # Adjust to "cpu" if GPU unavailable
    outputs = model.generate(**inputs, max_new_tokens=max_tokens)
    return tokenizer.decode(outputs[0])

# Gradio interface
interface = gr.Interface(
    fn=generate_response,
    inputs=[
        gr.Textbox(lines=2, label="Enter your prompt"),
        gr.Slider(10, 100, step=10, value=50, label="Max Tokens")
    ],
    outputs="text",
    title="NVIDIA NVLM-D-72B Demo",
    description="Generate text using NVIDIA's NVLM-D-72B model."
)
if __name__ == "__main__":
    interface.launch()


# import gradio as gr
# from huggingface_hub import InferenceClient

# """
# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# """
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


# def respond(
#     message,
#     history: list[tuple[str, str]],
#     system_message,
#     max_tokens,
#     temperature,
#     top_p,
# ):
#     messages = [{"role": "system", "content": system_message}]

#     for val in history:
#         if val[0]:
#             messages.append({"role": "user", "content": val[0]})
#         if val[1]:
#             messages.append({"role": "assistant", "content": val[1]})

#     messages.append({"role": "user", "content": message})

#     response = ""

#     for message in client.chat_completion(
#         messages,
#         max_tokens=max_tokens,
#         stream=True,
#         temperature=temperature,
#         top_p=top_p,
#     ):
#         token = message.choices[0].delta.content

#         response += token
#         yield response


# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# demo = gr.ChatInterface(
#     respond,
#     additional_inputs=[
#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
#         gr.Slider(
#             minimum=0.1,
#             maximum=1.0,
#             value=0.95,
#             step=0.05,
#             label="Top-p (nucleus sampling)",
#         ),
#     ],
# )


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