Update app.py

app.py

@@ -7,8 +7,9 @@ from transformers import pipeline, set_seed
 # 设置随机种子
 set_seed(42)
 
-# 尝试从环境变量获取 token
-HF_TOKEN = os.environ.get('HF_TOKEN', '')
+# 从环境变量获取 token
+HF_TOKEN = os.environ.get('HF_TOKEN')
+print(f"Token 状态: {'已设置' if HF_TOKEN else '未设置'}")
 
 # 初始化模型管道
 try:
@@ -19,16 +20,16 @@ try:
         torch_dtype="auto"
     )
     MODEL_LOADED = True
-    print("模型加载成功")
+    print("✅ 本地模型加载成功")
 except Exception as e:
-    print(f"模型加载失败: {e}")
+    print(f"❌ 本地模型加载失败: {e}")
     MODEL_LOADED = False
     protein_generator = None
 
 def generate_with_local_model(instruction, max_length=100):
     """使用本地加载的模型生成蛋白质序列"""
     if not MODEL_LOADED or protein_generator is None:
-        return "模型未正确加载，请检查控制台日志"
+        return "❌ 模型未正确加载，请检查控制台日志"
     
     try:
         result = protein_generator(
@@ -41,17 +42,17 @@ def generate_with_local_model(instruction, max_length=100):
         )
         
         if result and len(result) > 0:
-            return result[0]['generated_text']
+            return f"✅ 生成成功:\n\n{result[0]['generated_text']}"
         else:
-            return "生成失败，未获得有效结果"
+            return "❌ 生成失败，未获得有效结果"
             
     except Exception as e:
-        return f"生成过程中出现错误: {str(e)}"
+        return f"❌ 生成过程中出现错误: {str(e)}"
 
 def generate_with_api(instruction, max_length=100):
     """使用HuggingFace API生成蛋白质序列"""
     if not HF_TOKEN:
-        return "未设置 HuggingFace Token，无法使用 API 功能。请在环境变量中设置 HF_TOKEN。"
+        return "❌ 未设置 HuggingFace Token，无法使用 API 功能"
     
     try:
         API_URL = "https://api-inference.huggingface.co/models/mzcwd/ProtTeX"
@@ -71,27 +72,36 @@ def generate_with_api(instruction, max_length=100):
             }
         }
         
-        response = requests.post(API_URL, headers=headers, json=payload, timeout=30)
+        print("🔄 正在调用 API...")
+        response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
         
         if response.status_code == 200:
             result = response.json()
             if isinstance(result, list) and len(result) > 0:
-                return result[0].get('generated_text', '生成成功但无内容返回')
-            return str(result)
+                generated_text = result[0].get('generated_text', '生成成功但无内容返回')
+                return f"✅ API 生成成功:\n\n{generated_text}"
+            return f"✅ API 响应: {str(result)}"
         else:
-            return f"API请求失败 (状态码: {response.status_code})\n响应: {response.text}"
+            error_msg = f"❌ API请求失败 (状态码: {response.status_code})"
+            if response.text:
+                error_msg += f"\n详细信息: {response.text}"
+            return error_msg
             
     except requests.exceptions.Timeout:
-        return "请求超时，请稍后重试"
+        return "⏰ 请求超时，请稍后重试"
     except Exception as e:
-        return f"API调用错误: {str(e)}"
+        return f"❌ API调用错误: {str(e)}"
 
 def generate_protein(instruction, max_length=100):
     """主生成函数"""
-    yield "正在处理您的请求..."
+    yield "🔄 正在处理您的请求..."
     time.sleep(0.5)
     
     try:
+        # 显示当前模式
+        mode = "本地模型" if MODEL_LOADED else "API调用"
+        yield f"🔄 使用 {mode} 处理中..."
+        
         # 优先使用本地模型，失败时使用API
         if MODEL_LOADED:
             result = generate_with_local_model(instruction, max_length)
@@ -101,25 +111,24 @@ def generate_protein(instruction, max_length=100):
         yield result
         
     except Exception as e:
-        yield f"生成过程中出现未预期错误: {str(e)}"
+        yield f"❌ 生成过程中出现未预期错误: {str(e)}"
 
-# 创建界面
-with gr.Blocks(
-    title="ProtTeX 蛋白质生成器",
-    theme=gr.themes.Soft()
-) as demo:
+# 创建界面 - 移除了不兼容的参数
+with gr.Blocks(title="ProtTeX 蛋白质生成器") as demo:
     
     gr.Markdown("""
     # 🧬 ProtTeX 蛋白质生成器
     
-    使用自然语言指令生成蛋白质序列。输入您想要的蛋白质特性描述，AI将生成相应的蛋白质序列。
+    **使用自然语言指令生成蛋白质序列**
+    
+    *当前模式: {'🧠 本地模型' if MODEL_LOADED else '🌐 API 调用'} | Token: {'✅ 已设置' if HF_TOKEN else '❌ 未设置'}*
     """)
     
     with gr.Row():
-        with gr.Column():
+        with gr.Column(scale=1):
             instruction = gr.Textbox(
-                label="蛋白质生成指令",
-                placeholder="例如：Generate a protein with alpha-helical structure",
+                label="🧪 蛋白质生成指令",
+                placeholder="例如：Generate a protein with alpha-helical structure for membrane binding",
                 lines=3,
                 value="Generate a hydrophobic transmembrane protein sequence"
             )
@@ -129,33 +138,54 @@ with gr.Blocks(
                 maximum=300,
                 value=150,
                 step=10,
-                label="序列最大长度"
+                label="📏 序列最大长度"
             )
             
-            generate_btn = gr.Button("🚀 生成蛋白质序列", variant="primary")
-        
-        with gr.Column():
+            generate_btn = gr.Button(
+                "🚀 生成蛋白质序列",
+                variant="primary"
+            )
+            
+        with gr.Column(scale=1):
             output = gr.Textbox(
-                label="生成的蛋白质序列",
-                lines=6,
-                show_copy_button=True
+                label="🧬 生成的蛋白质序列",
+                lines=8
+                # 移除了 show_copy_button 参数
             )
     
-    gr.Markdown("### 示例指令")
-    gr.Examples(
+    # 示例部分
+    gr.Markdown("### 💡 示例指令（点击试用）")
+    
+    examples = gr.Examples(
         examples=[
             ["Generate a hydrophobic transmembrane protein sequence"],
             ["Create a water-soluble protein with beta-sheet structure"],
-            ["Design a protein with enzymatic activity for hydrolysis"]
+            ["Design a protein with enzymatic activity for hydrolysis"],
+            ["Generate a stable protein for high temperature environments"]
         ],
-        inputs=instruction
+        inputs=[instruction]
     )
     
+    # 连接按钮事件
     generate_btn.click(
         fn=generate_protein,
         inputs=[instruction, max_length],
-        outputs=output
+        outputs=[output]
     )
+    
+    # 状态信息
+    gr.Markdown(f"""
+    ---
+    **系统状态**:
+    - 运行模式: {'🧠 本地模型' if MODEL_LOADED else '🌐 API 调用'}
+    - Token 状态: {'✅ 已设置' if HF_TOKEN else '❌ 未设置'}
+    - 硬件: CPU Basic
+    
+    *基于 [mzcwd/ProtTeX](https://huggingface.co/mzcwd/ProtTeX) 模型*
+    """)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        server_name="0.0.0.0",
+        share=False
+    )