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



MODEL_LIST = ["THUDM/glm-4v-9b"]
HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = os.environ.get("MODEL_ID")
MODEL_NAME = MODEL_ID.split("/")[-1]

TITLE = "<h1><center>VL-Chatbox</center></h1>"

DESCRIPTION = f'<h3><center>MODEL: <a href="https://hf.co/{MODEL_ID}">{MODEL_NAME}</a></center></h3>'

CSS = """
.duplicate-button {
  margin: auto !important;
  color: white !important;
  background: black !important;
  border-radius: 100vh !important;
}
"""

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True
).to(0)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model.eval()


@spaces.GPU()
def stream_chat(message, history: list, temperature: float, max_length: int):
    print(f'message is - {message}')
    print(f'history is - {history}')
    conversation = []
    if message["files"]:
        image = Image.open(message["files"][-1]).convert('RGB')
        conversation.append({"role": "user", "image": image, "content": message['text']})
    else:
        if len(history) == 0:
            raise gr.Error("Please upload an image first.")
            image = None
        else:
            image = Image.open(history[0][0][0])
            for prompt, answer in history:
                if answer is None:
                    conversation.extend([{"role": "user", "content": prompt},{"role": "assistant", "content": ""}])
                else:
                    conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
            conversation.append({"role": "user", "image": image, "content": message['text']})
    print(f"Conversation is -\n{conversation}")

    input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        max_length=max_length,
        streamer=streamer,
        do_sample=True,
        top_k=1,
        temperature=temperature,
        repetition_penalty=1.2,
    )
    gen_kwargs = {**input_ids, **generate_kwargs}

    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=gen_kwargs)
        thread.start()
        buffer = ""
        for new_text in streamer:
            buffer += new_text
            yield buffer
 



chatbot = gr.Chatbot(height=450)
chat_input = gr.MultimodalTextbox(
    interactive=True,
    file_types=["image"],
    placeholder="Enter message or upload file...",
    show_label=False,

)
EXAMPLES = [
        [{"text": "Describe it in great detailed.", "files": ["./laptop.jpg"]}],
        [{"text": "Describe it in great detailed.", "files": ["./hotel.jpg"]}],
        [{"text": "Describe it in great detailed.", "files": ["./spacecat.png"]}]
]

with gr.Blocks(css=CSS) as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        multimodal=True,
        textbox=chat_input,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=4096,
                step=1,
                value=1024,
                label="Max Length",
                render=False,
            ),
        ],
    ),
    gr.Examples(EXAMPLES,[chat_input])


if __name__ == "__main__":
    demo.queue(api_open=False).launch(show_api=False, share=False)