cklean up interfaces

app.py

@@ -1,5 +1,4 @@
 import os
-
 import gradio as gr
 import pandas as pd
 import plotly.express as px
@@ -15,13 +14,15 @@ def run_shell_script(secret_key):
     # 记得在 Space 的 Settings -> Variables and secrets 里设置一个环境变量叫 "RUN_KEY"
     expected_key = os.environ.get("RUN_KEY")
     
+    if not expected_key:
+        return "❌ 鉴权失败：服务端未配置 RUN_KEY 环境变量！"
+        
     if secret_key != expected_key:
         return "❌ 鉴权失败：暗号错误！"
 
     print("收到指令，开始运行脚本...")
     
     # 2. 核心：运行 .sh 文件
-    # capture_output=True 可以让我们看到脚本输出的日志
     try:
         result = subprocess.run(
             ["./gpu_info_collector.sh"], 
@@ -30,25 +31,16 @@ def run_shell_script(secret_key):
             text=True
         )
         log_output = f"Standard Output:\n{result.stdout}\n\nError Output:\n{result.stderr}"
-        print(log_output) # 这会打印到 Space 的 Logs 里
+        print(log_output) 
         return f"✅ 脚本运行完毕！\n{log_output}"
     except Exception as e:
         return f"⚠️ 运行出错: {str(e)}"
 
-
-
 # ==========================================
 # 1. Data Reading Engine
 # ==========================================
 
-
 def clean_and_read_file(file_path):
-    """
-    Robust file reader:
-    1. Handles .xlsx masquerading as .csv
-    2. Cleans garbage tags like 
-    3. Fixes broken lines
-    """
     if not file_path or not os.path.exists(file_path):
         return pd.DataFrame()
 
@@ -122,7 +114,6 @@ def clean_and_read_file(file_path):
 # 2. Data Processing
 # ==========================================
 
-
 def process_gpu_data(df):
     if df.empty:
         return df
@@ -168,7 +159,6 @@ def process_llm_data(df):
 # 3. Plotting Logic
 # ==========================================
 
-
 def plot_gpu_trends(df):
     if df is None or df.empty or 'Rent_Price_Num' not in df.columns:
         return None
@@ -177,7 +167,8 @@ def plot_gpu_trends(df):
     if plot_df.empty:
         return None
 
-    chip_col = 'Chip' if 'Chip' in df.columns else df.columns[1]
+    # 防御性修复：防止 df 列数不够导致 Index 出界
+    chip_col = 'Chip' if 'Chip' in df.columns else (df.columns[1] if len(df.columns) > 1 else None)
 
     fig = px.line(plot_df,
                   x='Date',
@@ -192,18 +183,15 @@ def plot_gpu_trends(df):
     return fig
 
 def plot_llm_trends(df):
-    """Plot trends for all columns, no selection needed anymore"""
     if df is None or df.empty:
         return None
 
-    # Automatically select all columns except Date
     value_vars = [c for c in df.columns if c != 'Date']
     if not value_vars:
         return None
 
     plot_df = df[['Date'] + value_vars].copy().dropna(subset=['Date'])
 
-    # Melt
     df_long = plot_df.melt(id_vars=['Date'], var_name='Model', value_name='Price')
 
     fig = px.line(
@@ -248,10 +236,9 @@ with gr.Blocks(title="AI Price Tracker") as demo:
                 with gr.Accordion("Data Preview", open=False):
                     gpu_table = gr.DataFrame()
 
-        # LLM Tab (Updated: No Filter)
+        # LLM Tab
         with gr.TabItem("LLM Prices"):
             with gr.Row():
-                # Display chart directly, no column division
                 with gr.Column(scale=1):
                     llm_plot = gr.Plot(label="Price Trend")
 
@@ -261,39 +248,32 @@ with gr.Blocks(title="AI Price Tracker") as demo:
 
     # --- Initialization Logic ---
     def init_on_load():
-        # Load GPU
         g_df, g_fig = load_gpu_pipeline()
-
-        # Load LLM (No checkbox needed anymore)
         l_df, l_fig = load_llm_pipeline()
+        return g_fig, g_df, l_fig, l_df
 
-        return (
-            g_fig,      # gpu_plot
-            g_df,       # gpu_table
-            l_fig,      # llm_plot
-            l_df        # llm_table
-        )
-
-    # Bind load event
     demo.load(
         init_on_load,
         inputs=None,
-        outputs=[
-            gpu_plot,
-            gpu_table,
-            llm_plot,
-            llm_table
-        ]
+        outputs=[gpu_plot, gpu_table, llm_plot, llm_table]
     )
 
-    # Create a hidden Gradio Interface for the API endpoint
-    # This will automatically generate an API endpoint for run_shell_script
-    gr.Interface(
-        fn=run_shell_script,
-        inputs=gr.Textbox(label="Secret Key", type="password"),
-        outputs=gr.Textbox(label="Script Output"),
-        api_name="run_collector",  # Explicitly set API name
-    )
+    # ---------------- 修复区 ----------------
+    # 替换原本违规的 gr.Interface 为一个隐形的组件组 (visible=False)
+    # 这样可以在不破坏 UI 布局的前提下，成功暴露名为 "run_collector" 的 API
+    with gr.Group(visible=False):
+        api_input = gr.Textbox(label="Secret Key")
+        api_output = gr.Textbox(label="Script Output")
+        api_btn = gr.Button("Run")
+        
+        # 绑定点击事件并赋予 API Name
+        api_btn.click(
+            fn=run_shell_script, 
+            inputs=api_input, 
+            outputs=api_output, 
+            api_name="run_collector"
+        )
+    # ----------------------------------------
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch(share=True)