Update app.py

app.py

@@ -5,21 +5,25 @@ import gradio as gr
 from PIL import Image
 from huggingface_hub import login
 import os
+import warnings
+
+# 抑制警告
+warnings.filterwarnings("ignore", category=RuntimeWarning)
 
 # ========== 使用你的 secret 名称 fmv 登录 ==========
 token = os.getenv("fmv")
 if token:
     login(token=token)
-    print("成功使用 token 登录!")
+    print("Successfully logged in with token!")
 else:
-    print("警告:未找到 token")
+    print("Warning: Token not found")
 # ==========================================
 
-# Hugging Face 模型仓库路径
+# Hugging Face model repository path
 model_path = "hiko1999/Qwen2-Wildfire-2B"
 
-# 加载模型和 processor
-print(f"正在加载模型: {model_path}")
+# Load model and processor
+print(f"Loading model: {model_path}")
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = Qwen2VLForConditionalGeneration.from_pretrained(
     model_path, 
@@ -27,27 +31,27 @@ model = Qwen2VLForConditionalGeneration.from_pretrained(
     device_map="cpu"
 )
 processor = AutoProcessor.from_pretrained(model_path)
-print("模型加载完成!")
+print("Model loaded successfully!")
 
-# 定义预测函数
+# Define prediction function
 def predict(image):
-    """处理图片并生成描述"""
+    """Process image and generate description"""
     if image is None:
-        return "错误:未上传图片"
+        return "Error: No image uploaded"
     
     try:
-        # 构建消息
+        # Build message with English prompt
         messages = [
             {
                 "role": "user",
                 "content": [
                     {"type": "image", "image": image},
-                    {"type": "text", "text": "请描述这张图片中的火灾情况。"}
+                    {"type": "text", "text": "Describe this wildfire scene in English. Include details about the fire intensity, affected area, and visible environmental conditions."}
                 ]
             }
         ]
         
-        # 处理输入
+        # Process input
         text = processor.apply_chat_template(
             messages, 
             tokenize=False, 
@@ -62,10 +66,10 @@ def predict(image):
             return_tensors="pt"
         )
         
-        # 确保在 CPU 上运行
+        # Ensure running on CPU
         inputs = inputs.to("cpu")
         
-        # 生成输出
+        # Generate output
         generated_ids = model.generate(
             **inputs, 
             max_new_tokens=256,
@@ -73,7 +77,7 @@ def predict(image):
             temperature=0.7
         )
         
-        # 解码输出
+        # Decode output
         generated_ids_trimmed = [
             out_ids[len(in_ids):] 
             for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
@@ -87,23 +91,23 @@ def predict(image):
         return output_text[0]
         
     except Exception as e:
-        return f"预测失败: {str(e)}"
+        return f"Prediction failed: {str(e)}"
 
-# Gradio 界面函数
+# Gradio interface function
 def gradio_interface(image):
-    """Gradio 界面的主函数"""
+    """Main function for Gradio interface"""
     result = predict(image)
     return result
 
-# 创建 Gradio 界面 (移除了 theme 和 allow_flagging)
+# Create Gradio interface (all in English)
 interface = gr.Interface(
     fn=gradio_interface,
-    inputs=gr.Image(type="pil", label="上传火灾图片"),
-    outputs=gr.Textbox(label="AI 分析结果", lines=10),
-    title="🔥 火灾场景智能分析系统",
-    description="上传火灾相关图片,AI 将自动分析并描述火灾情况。"
+    inputs=gr.Image(type="pil", label="Upload Wildfire Image"),
+    outputs=gr.Textbox(label="AI Analysis Result", lines=10),
+    title="🔥 Wildfire Scene Analysis System",
+    description="Upload a wildfire-related image and AI will automatically analyze and describe the fire situation in English."
 )
 
-# 启动接口 (把 theme 移到这里)
+# Launch interface
 if __name__ == "__main__":
     interface.launch(share=False)