app.py

import gradio as gr
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq

# 模型与处理器
model_id = "Qwen/Qwen1.5-VL-Chat"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    device_map="auto",  # 自动分配 GPU 或 CPU
    trust_remote_code=True,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    **{"disable_exllama": True}  # 防止加载失败
).eval()

# 推理函数
def chat(image, question):
    if image is None or question.strip() == "":
        return "請上傳圖片並輸入問題。"
    inputs = processor(text=question, images=image, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=512)
    answer = processor.batch_decode(outputs, skip_special_tokens=True)[0]
    return answer.strip()

# 界面設計
with gr.Blocks(title="Qwen1.5-VL 圖文問答 Demo") as demo:
    gr.Markdown("## 🧠 Qwen1.5-VL 圖文問答 Demo")
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="pil", label="📷 請上傳圖片")
            question_input = gr.Textbox(label="請輸入問題", placeholder="例如：這是什麼地方？")
            submit_btn = gr.Button("Submit", variant="primary")
            clear_btn = gr.Button("Clear")
        with gr.Column():
            answer_output = gr.Textbox(label="💬 答案", lines=8)

    submit_btn.click(fn=chat, inputs=[image_input, question_input], outputs=answer_output)
    clear_btn.click(lambda: ("", "", ""), outputs=[image_input, question_input, answer_output])

# 啟動服務
demo.launch(share=True)  # 如果你不想公開訪問可以改為 share=False