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 = "

VL-Chatbox

" DESCRIPTION = f'

A SPACE FOR VLM MODELS


MODEL NOW: {MODEL_NAME}

' CSS = """ .duplicate-button { margin: auto !important; color: white !important; background: black !important; border-radius: 100vh !important; } h1 { text-align: center; display: block; } p { text-align: center; } """ 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, top_p: float, top_k: int, penalty: float): 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 conversation.append({"role": "user", "content": message['text']}) else: image = Image.open(history[0][0][0]) for prompt, answer in history: if answer is None: conversation.extend([{"role": "user", "content": ""},{"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_p=top_p, top_k=top_k, temperature=temperature, repetition_penalty=penalty, eos_token_id=[151329, 151336, 151338], ) 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 detailed", "files": ["./laptop.jpg"]}], [{"text": "Where it is?", "files": ["./hotel.jpg"]}], [{"text": "Is it real?", "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=8192, step=1, value=1024, label="Max Length", render=False, ), with gr.Row(): gr.Slider( minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="top_p", render=False, ), gr.Slider( minimum=1, maximum=20, step=1, value=10, label="top_k", render=False, ), gr.Slider( minimum=0.0, maximum=2.0, step=0.1, value=1.0, label="Repetition penalty", render=False, ), ], ), gr.Examples(EXAMPLES,[chat_input]) if __name__ == "__main__": demo.queue(api_open=False).launch(show_api=False, share=False)