import spaces import os import re import time import gradio as gr import torch from transformers import AutoModelForCausalLM from transformers import TextIteratorStreamer from threading import Thread model_name = 'AIDC-AI/Ovis1.6-Gemma2-9B' # load model model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, multimodal_max_length=8192, trust_remote_code=True).to(device='cuda') text_tokenizer = model.get_text_tokenizer() visual_tokenizer = model.get_visual_tokenizer() streamer = TextIteratorStreamer(text_tokenizer, skip_prompt=True, skip_special_tokens=True) image_placeholder = '' cur_dir = os.path.dirname(os.path.abspath(__file__)) def submit_chat(chatbot, text_input): response = '' chatbot.append((text_input, response)) return chatbot ,'' @spaces.GPU def ovis_chat(chatbot, image_input): # preprocess inputs conversations = [] response = "" text_input = chatbot[-1][0] for query, response in chatbot[:-1]: conversations.append({ "from": "human", "value": query }) conversations.append({ "from": "gpt", "value": response }) text_input = text_input.replace(image_placeholder, '') conversations.append({ "from": "human", "value": text_input }) if image_input is not None: conversations[0]["value"] = image_placeholder + '\n' + conversations[0]["value"] prompt, input_ids, pixel_values = model.preprocess_inputs(conversations, [image_input]) attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id) input_ids = input_ids.unsqueeze(0).to(device=model.device) attention_mask = attention_mask.unsqueeze(0).to(device=model.device) if image_input is None: pixel_values = [None] else: pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)] with torch.inference_mode(): gen_kwargs = dict( max_new_tokens=512, do_sample=False, top_p=None, top_k=None, temperature=None, repetition_penalty=None, eos_token_id=model.generation_config.eos_token_id, pad_token_id=text_tokenizer.pad_token_id, use_cache=True ) response = "" thread = Thread(target=model.generate, kwargs={"inputs": input_ids, "pixel_values": pixel_values, "attention_mask": attention_mask, "streamer": streamer, **gen_kwargs}) thread.start() for new_text in streamer: response += new_text chatbot[-1][1] = response yield chatbot thread.join() # debug print('*'*60) print('*'*60) print('OVIS_CONV_START') for i, (request, answer) in enumerate(chatbot[:-1], 1): print(f'Q{i}:\n {request}') print(f'A{i}:\n {answer}') print('New_Q:\n', text_input) print('New_A:\n', response) print('OVIS_CONV_END') def clear_chat(): return [], None, "" with open(f"{cur_dir}/resource/logo.svg", "r", encoding="utf-8") as svg_file: svg_content = svg_file.read() font_size = "2.5em" svg_content = re.sub(r'(]*)(>)', rf'\1 height="{font_size}" style="vertical-align: middle; display: inline-block;"\2', svg_content) html = f"""

{svg_content} {model_name.split('/')[-1]}

Ovis has been open-sourced on 😊 Huggingface and 🌟 GitHub. If you find Ovis useful, a like❤️ or a star🌟 would be appreciated.
""" latex_delimiters_set = [{ "left": "\\(", "right": "\\)", "display": False }, { "left": "\\begin{equation}", "right": "\\end{equation}", "display": True }, { "left": "\\begin{align}", "right": "\\end{align}", "display": True }, { "left": "\\begin{alignat}", "right": "\\end{alignat}", "display": True }, { "left": "\\begin{gather}", "right": "\\end{gather}", "display": True }, { "left": "\\begin{CD}", "right": "\\end{CD}", "display": True }, { "left": "\\[", "right": "\\]", "display": True }] text_input = gr.Textbox(label="prompt", placeholder="Enter your text here...", lines=1, container=False) with gr.Blocks(title=model_name.split('/')[-1], theme=gr.themes.Ocean()) as demo: gr.HTML(html) with gr.Row(): with gr.Column(scale=3): image_input = gr.Image(label="image", height=350, type="pil") gr.Examples( examples=[ [f"{cur_dir}/examples/case0.png", "Find the area of the shaded region."], [f"{cur_dir}/examples/case1.png", "explain this model to me."], [f"{cur_dir}/examples/case2.png", "What is net profit margin as a percentage of total revenue?"], ], inputs=[image_input, text_input] ) with gr.Column(scale=7): chatbot = gr.Chatbot(label="Ovis", layout="panel", height=600, show_copy_button=True, latex_delimiters=latex_delimiters_set) text_input.render() with gr.Row(): send_btn = gr.Button("Send", variant="primary") clear_btn = gr.Button("Clear", variant="secondary") send_click_event = send_btn.click(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot) submit_event = text_input.submit(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot) clear_btn.click(clear_chat, outputs=[chatbot, image_input, text_input]) demo.launch()