Add Gradio application

app.py

@@ -0,0 +1,48 @@
+import torch
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import gradio as gr
+from PIL import Image
+
+# Hugging Face 模型仓库路径
+model_path = "hiko1999/Qwen2-Wildfire-VL-2B-Instruct"  # 替换为你的模型路径
+
+# 加载 Hugging Face 上的模型和 processor
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = Qwen2VLForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map="auto")
+processor = AutoProcessor.from_pretrained(model_path)
+
+# 定义预测函数
+def predict(image):
+    # 将上传的图片处理为模型需要的格式
+    messages = [{"role": "user",
+                 "content": [{"type": "image", "image": image}, {"type": "text", "text": "Describe this image."}]}]
+
+    # 处理图片输入
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
+    inputs = inputs.to("cuda")  # 转移到GPU
+
+    # 生成模型输出
+    generated_ids = model.generate(**inputs, max_new_tokens=128)
+    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+    output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True,
+                                         clean_up_tokenization_spaces=False)
+
+    return output_text[0]  # 返回生成的文本
+
+# Gradio界面
+def gradio_interface(image):
+    result = predict(image)
+    return f"预测结果：{result}"
+
+# 创建Gradio接口
+interface = gr.Interface(fn=gradio_interface,
+                         inputs=gr.Image(type="pil"),  # 输入的图像
+                         outputs="text",  # 输出结果
+                         title="火灾场景多模态模型预测",
+                         description="上传图片进行火灾预测。")
+
+# 启动接口
+interface.launch()