Create app.py

app.py

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+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