app.py

import gradio as gr
import torch
from transformers import LlavaProcessor, LlavaForConditionalGeneration
from PIL import Image

# 加载模型
model_id = "llava-hf/llava-v1.5-7b"
processor = LlavaProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)

def llava_infer(image, text):
    if image is None or text.strip() == "":
        return "请提供图片和文本输入"
    
    # 处理输入
    inputs = processor(text=text, images=image, return_tensors="pt").to("cuda")
    
    # 生成输出
    with torch.no_grad():
        output = model.generate(**inputs, max_new_tokens=100)
    
    result = processor.batch_decode(output, skip_special_tokens=True)[0]
    return result

# 创建 Gradio 界面
iface = gr.Interface(
    fn=llava_infer,
    inputs=[gr.Image(type="pil"), gr.Textbox(placeholder="输入文本...")],
    outputs="text",
    title="LLaVA Web UI",
    description="上传图片并输入文本，LLaVA 将返回回答"
)

iface.launch()