170 lines
8.1 KiB
Python
170 lines
8.1 KiB
Python
import os
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import sys
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__package__ = "scripts"
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import argparse
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import torch
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import warnings
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import gradio as gr
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from queue import Queue
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from threading import Thread
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from PIL import Image
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from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
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from model.model_vlm import MiniMindVLM, VLMConfig
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from transformers import logging as hf_logging
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hf_logging.set_verbosity_error()
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warnings.filterwarnings('ignore')
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def scan_vlm_models(base_dir):
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models = {}
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base_dir = os.path.abspath(base_dir)
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for d in sorted(os.listdir(base_dir), reverse=True):
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full_path = os.path.join(base_dir, d)
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if not os.path.isdir(full_path) or d.startswith('.') or d.startswith('_'):
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continue
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files = os.listdir(full_path)
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has_model = any(f.endswith(('.bin', '.safetensors')) for f in files) or 'model.safetensors.index.json' in files
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if has_model:
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models[d] = full_path
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return models
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def load_vlm_model(model_path):
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global model, tokenizer, preprocess, lm_config, current_model_name
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
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model.vision_encoder, model.processor = MiniMindVLM.get_vision_model(vision_model_path)
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if model.vision_encoder is None: raise FileNotFoundError(f"视觉编码器未找到: {vision_model_path}")
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preprocess = model.processor
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lm_config = model.config
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model = model.half().eval().to(device)
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model.vision_encoder = model.vision_encoder.to(device)
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current_model_name = os.path.basename(model_path)
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param_str = f'{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M'
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print(f'已加载 {current_model_name}，参数量：{param_str}')
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return f"已加载: {current_model_name} ({param_str})"
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class CustomStreamer(TextStreamer):
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def __init__(self, tokenizer, queue):
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super().__init__(tokenizer, skip_prompt=True, skip_special_tokens=True)
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self.queue = queue
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self.tokenizer = tokenizer
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def on_finalized_text(self, text: str, stream_end: bool = False):
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self.queue.put(text)
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if stream_end:
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self.queue.put(None)
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def chat(prompt, current_image_path=None):
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global temperature, top_p
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pixel_values = None
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if current_image_path:
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image = Image.open(current_image_path).convert('RGB')
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pixel_values = {k: v.to(model.device) for k, v in MiniMindVLM.image2tensor(image, preprocess).items()}
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prompt = f'{lm_config.image_special_token * lm_config.image_token_len}\n{prompt}'
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messages = [{"role": "user", "content": prompt}]
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new_prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)[-max_seq_len + 1:]
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with torch.no_grad():
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inputs = tokenizer(
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new_prompt,
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return_tensors="pt",
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truncation=True
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).to(device)
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queue = Queue()
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streamer = CustomStreamer(tokenizer, queue)
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def _generate():
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model.generate(
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inputs.input_ids,
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max_new_tokens=max_seq_len,
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do_sample=True,
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temperature=temperature,
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top_p=top_p,
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attention_mask=inputs.attention_mask,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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streamer=streamer,
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pixel_values=pixel_values
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)
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Thread(target=_generate).start()
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while True:
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text = queue.get()
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if text is None:
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break
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yield text
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def launch_gradio_server(server_name="0.0.0.0", server_port=7788):
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global temperature, top_p
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temperature = args.temperature
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top_p = args.top_p
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def respond(message, history):
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if not message or not message.get("text"):
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yield history + [{"role": "assistant", "content": "请输入问题"}]
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return
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files = message.get("files", [])
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img_path = files[0] if files else None
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text = message["text"]
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if img_path:
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history = history + [{"role": "user", "content": {"path": img_path}}, {"role": "user", "content": text}]
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else:
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history = history + [{"role": "user", "content": text}]
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response = ''
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for chunk in chat(text, img_path):
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response += chunk
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yield history + [{"role": "assistant", "content": response}]
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with gr.Blocks(title="minimind-3v", css="#chatbox img{max-width:120px!important;max-height:120px!important;border-radius:8px} div.full-container{display:flex!important;flex-direction:row!important;flex-wrap:nowrap!important;align-items:center!important} div.full-container>.thumbnails{flex-shrink:0!important;max-width:50px!important} div.full-container>.input-container{flex:1!important} .input-wrapper{display:flex!important;flex-direction:row!important;flex-wrap:nowrap!important;align-items:center!important} .input-wrapper>.thumbnails{flex-shrink:0!important;max-width:50px!important} .input-wrapper>.input-row{flex:1!important} .thumbnail-image{max-width:40px!important;max-height:40px!important} textarea{overflow-y:hidden!important} #chatbox,#chatbox *{scrollbar-width:none!important;-ms-overflow-style:none!important} #chatbox::-webkit-scrollbar,#chatbox *::-webkit-scrollbar{display:none!important;width:0!important;height:0!important}") as demo:
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gr.HTML('<div style="display:flex;flex-direction:column;align-items:center;justify-content:center;margin:-8px 0 -6px 0"><div style="display:flex;align-items:center;justify-content:center;gap:8px"><img src="https://www.modelscope.cn/api/v1/studio/gongjy/MiniMind/repo?Revision=master&FilePath=images%2Flogo2.png&View=true" style="height:30px"><span style="font-size:1.5rem;font-weight:bold;font-style:italic">minimind-3v</span></div><div style="color:#bbb;font-style:italic;margin:0">AI-generated content may be inaccurate, please verify</div></div>')
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try:
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chatbot = gr.Chatbot(label="", height=560, elem_id="chatbox", type="messages")
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except TypeError:
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chatbot = gr.Chatbot(label="", height=560, elem_id="chatbox")
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msg = gr.MultimodalTextbox(placeholder="输入问题，点击📎上传图片", show_label=False, submit_btn="发送")
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with gr.Row():
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model_dropdown = gr.Dropdown(choices=list(model_dict.keys()), value=current_model_name, show_label=False, scale=1)
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status_text = gr.Textbox(value=f"已加载: {current_model_name}", show_label=False, interactive=False, scale=2)
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def on_model_change(name):
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return load_vlm_model(model_dict[name])
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model_dropdown.change(on_model_change, [model_dropdown], [status_text])
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msg.submit(respond, [msg, chatbot], chatbot)
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demo.launch(server_name=server_name, server_port=server_port)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="Chat with MiniMind-V")
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parser.add_argument('--load_from', default='./', type=str, help="transformers模型扫描目录")
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parser.add_argument('--vision_model', default='../model/siglip2-base-p16-256-ve', type=str, help="视觉编码器路径")
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parser.add_argument('--temperature', default=0.7, type=float, help="生成温度")
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parser.add_argument('--top_p', default=0.95, type=float, help="nucleus采样阈值")
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parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help="运行设备")
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parser.add_argument('--max_seq_len', default=8192, type=int, help="最大序列长度")
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args = parser.parse_args()
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device = args.device
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max_seq_len = args.max_seq_len
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vision_model_path = args.vision_model
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model_dict = scan_vlm_models(args.load_from)
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if not model_dict:
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print(f"未在 {os.path.abspath(args.load_from)} 找到transformers模型")
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exit(1)
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current_model_name = list(model_dict.keys())[0]
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load_vlm_model(model_dict[current_model_name])
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launch_gradio_server(server_name="0.0.0.0", server_port=8888)
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