Update app.py

app.py

@@ -7,33 +7,79 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import gradio as gr
 
-def analyze_csv(file):
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+import tempfile
+import os
+
+# 主分析函数
+def analyze_csv(file, u, alpha1, alpha2):
     df = pd.read_csv(file.name)
+    df_original = df.copy()
+    df["Computed"] = df["Income"] * u + df["SpendingScore"] * alpha1 - df["Age"] * alpha2
 
-    # 统计分析
-    stats = df.describe().loc[["mean", "std", "min", "max"]].round(2).to_string()
+    output_path = os.path.join(tempfile.gettempdir(), "processed.csv")
+    df.to_csv(output_path, index=False)
 
-    # 生成图像：Income vs SpendingScore
+    image_path = os.path.join(tempfile.gettempdir(), "computed_plot.png")
     plt.figure(figsize=(6, 4))
-    plt.scatter(df["Income"], df["SpendingScore"], alpha=0.7)
-    plt.title("Income vs Spending Score")
+    plt.plot(df["Income"], df["Computed"], marker='o')
     plt.xlabel("Income")
-    plt.ylabel("Spending Score")
+    plt.ylabel("Computed Value")
+    plt.title("Income vs Computed")
     plt.grid(True)
-
-    img_path = "income_vs_score.png"
-    plt.savefig(img_path)
+    plt.tight_layout()
+    plt.savefig(image_path)
     plt.close()
 
-    return stats, img_path
+    return df_original, df, output_path, image_path
+
+
+# 示例数据定义（文件路径 + 参数）
+example_data = [
+    ["sample_table_0.csv", 0.0002, 0.4, 0.8],
+    ["sample_table_1.csv", 0.0001, 0.5, 1.0],
+    ["sample_table_2.csv", 0.00015, 0.6, 0.7],
+]
+
+
+# 构建 Gradio 界面
+with gr.Blocks() as demo:
+    gr.Markdown("## 📊 表格分析大模型")
+    gr.Markdown("上传 CSV 文件或点击示例，我将为你分析并可视化结果。")
+
+    with gr.Row():
+        file_input = gr.File(label="上传CSV文件", file_types=[".csv"])
+        u = gr.Number(label="u", value=0.0001)
+        alpha1 = gr.Number(label="alpha1", value=0.5)
+        alpha2 = gr.Number(label="alpha2", value=1.0)
+
+    run_btn = gr.Button("开始分析")
+
+    gr.Markdown("### ✅ 示例（点击自动加载）")
+    gr.Examples(
+        examples=example_data,
+        inputs=[file_input, u, alpha1, alpha2],
+        outputs=["original_table", "processed_table", "download", "image"],
+        fn=analyze_csv,
+        examples_per_page=3,
+        label="点击示例自动分析"
+    )
+
+    gr.Markdown("### 📄 原始 CSV")
+    original_table = gr.Dataframe(label="original_table")
+
+    gr.Markdown("### 📑 处理后 CSV")
+    processed_table = gr.Dataframe(label="processed_table")
+    download = gr.File(label="下载处理结果", elem_id="download")
+
+    gr.Markdown("### 📈 图表可视化")
+    image = gr.Image(label="image")
+
+    run_btn.click(fn=analyze_csv,
+                  inputs=[file_input, u, alpha1, alpha2],
+                  outputs=[original_table, processed_table, download, image])
 
-iface = gr.Interface(
-    fn=analyze_csv,
-    inputs=gr.File(label="Upload CSV File", file_types=[".csv"]),
-    outputs=[gr.Text(label="📊 Statistical Summary"), gr.Image(label="📈 Income vs Spending Score")],
-    title="📊 表格分析大模型",
-    description="上传一个CSV表格，我将输出统计分析结果并展示一张图表。"
-)
+demo.launch()
 
-if __name__ == "__main__":
-    iface.launch()