File size: 1,633 Bytes
6593745 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
# 使用你的设备
device = "cuda" if torch.cuda.is_available() else "cpu"
# 指定模型
model_id = "Qwen/Qwen2-VL-7B" # 或者 "Qwen/Qwen2-VL-Chat-7B"
# 加载模型和分词器
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map=device,
torch_dtype="auto",
trust_remote_code=True
).eval()
# 设定模型的生成参数
model.generation_config = GenerationConfig.from_pretrained(
model_id,
trust_remote_code=True
)
model.generation_config.do_sample = False # 禁用采样，使用 beam search
def respond(image, prompt, history):
# 使用模型的 chat 方法进行对话
response, history = model.chat(tokenizer, image, prompt, history=history)
return response, history
with gr.Blocks() as demo:
gr.Markdown(f"## Qwen2-VL-7B Demo (Model: {model_id})")
with gr.Row():
with gr.Column(scale=4):
image = gr.Image(type="pil", label="Image")
text_input = gr.Textbox(label="Prompt", placeholder="输入提示")
submit_button = gr.Button("Submit")
with gr.Column(scale=6):
chatbot = gr.Chatbot(label="Chatbot")
history = gr.State([])
submit_button.click(
respond,
inputs=[image, text_input, history],
outputs=[chatbot, history]
)
demo.queue().launch(server_name='0.0.0.0', server_port=7860, share=True) # 启用 share=True 以生成公开链接 |