import torch from PIL import Image import gradio as gr import spaces from transformers import AutoModel, AutoTokenizer import os os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" MODEL_LIST = ["openbmb/MiniCPM-Llama3-V-2_5","openbmb/MiniCPM-Llama3-V-2_5-int4"] 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'

MODEL: {MODEL_NAME}

' CSS = """ .duplicate-button { margin: auto !important; color: white !important; background: black !important; border-radius: 100vh !important; } """ model = AutoModel.from_pretrained( MODEL_ID, torch_dtype=torch.float16, 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_new_tokens: 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", "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", "content": message['text']}) print(f"Conversation is -\n{conversation}") generate_kwargs = dict( image=image, msgs=conversation, max_new_tokens=max_new_tokens, temperature=temperature, sampling=True, tokenizer=tokenizer, stream=True ) if temperature == 0: generate_kwargs["sampling"] = False response = model.chat(**generate_kwargs) generated_text = "" for new_text in response: generated_text += new_text yield(new_text, flush=True, end='') 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 new tokens", render=False, ), ], ), gr.Examples(EXAMPLES,[chat_input]) if __name__ == "__main__": demo.queue(api_open=False).launch(show_api=False, share=False)