Update app.py

app.py

@@ -1,64 +1,91 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from simple_inference import predict_location
+import os
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# 设置默认模型和引擎
+DEFAULT_MODEL = "Qwen/Qwen2.5-VL-7B-Instruct"
+DEFAULT_ENGINE = "transformers"
 
+def process_image(image, model_name, inference_engine):
+    """
+    处理上传的图片并返回预测结果
+    """
+    # 保存上传的图片到临时文件
+    temp_path = "temp_image.jpg"
+    image.save(temp_path)
+    
+    try:
+        # 调用预测函数
+        result = predict_location(
+            image_path=temp_path,
+            model_name=model_name,
+            inference_engine=inference_engine
+        )
+        
+        # 删除临时文件
+        os.remove(temp_path)
+        
+        return result
+    except Exception as e:
+        # 删除临时文件
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+        return f"发生错误: {str(e)}"
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
+# 创建Gradio界面
+with gr.Blocks(title="位置识别系统") as demo:
+    gr.Markdown("# 🏞️ 图片位置识别系统")
+    gr.Markdown("上传一张图片，系统将识别图片拍摄的国家和地区。")
+    
+    with gr.Row():
+        with gr.Column():
+            # 图片上传组件
+            image_input = gr.Image(type="pil", label="上传图片")
+            
+            # 模型选择
+            model_name = gr.Textbox(
+                label="模型名称",
+                value=DEFAULT_MODEL,
+                info="输入Hugging Face模型ID或本地模型路径"
+            )
+            
+            # 推理引擎选择
+            inference_engine = gr.Dropdown(
+                choices=["transformers", "vllm"],
+                value=DEFAULT_ENGINE,
+                label="推理引擎"
+            )
+            
+            # 提交按钮
+            submit_btn = gr.Button("开始识别", variant="primary")
+        
+        with gr.Column():
+            # 结果显示
+            output = gr.Textbox(
+                label="识别结果",
+                lines=10,
+                placeholder="识别结果将显示在这里..."
+            )
+    
+    # 设置提交按钮的回调函数
+    submit_btn.click(
+        fn=process_image,
+        inputs=[image_input, model_name, inference_engine],
+        outputs=output
+    )
+    
+    # 添加示例
+    gr.Examples(
+        examples=[
+            ["examples/example1.jpg", DEFAULT_MODEL, DEFAULT_ENGINE],
+            ["examples/example2.jpg", DEFAULT_MODEL, DEFAULT_ENGINE],
+        ],
+        inputs=[image_input, model_name, inference_engine],
+        outputs=output,
+        fn=process_image,
+        cache_examples=True,
+    )
 
+# 启动应用
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)