Update app.py

app.py

@@ -6,8 +6,10 @@ import io
 
 # --- 1. Function to process the uploaded file and generate stats/dataframe ---
 def analyze_csv(file):
+    """Reads a CSV file, calculates statistics, and prepares components for plotting."""
     if file is None:
-        return None, "Please upload a CSV file.", None, None
+        # Clear previous outputs if a file is unuploaded or if this is the initial state
+        return None, "Please upload a CSV file.", gr.Dropdown(choices=[], label="Select Column to Plot"), None
 
     try:
         # Read the uploaded file object (it's a temporary file path)
@@ -16,23 +18,24 @@ def analyze_csv(file):
         # Basic descriptive statistics
         stats = df.describe().round(2).T
         
-        # Convert stats to a readable string or keep as DataFrame
+        # Convert stats to markdown string for display
         stats_markdown = stats.to_markdown()
 
         # Get list of numeric columns for the dropdown
         numeric_cols = df.select_dtypes(include=['number']).columns.tolist()
         
-        # Return the full DataFrame, the statistics table, and the list of columns
+        # Return the full DataFrame, the statistics table, the updated column dropdown, and the DataFrame state
         return df, stats_markdown, gr.Dropdown(choices=numeric_cols, label="Select Column to Plot"), df
         
     except Exception as e:
         error_message = f"Error processing file: {e}"
-        # Return None for all outputs in case of error
+        # Return error message and clear other components
         return None, error_message, gr.Dropdown(choices=[], label="Select Column to Plot"), None
 
 
 # --- 2. Function to generate a plot for a selected column ---
 def generate_plot(df_state, column_name):
+    """Generates a distribution plot (histogram) for the selected column."""
     if df_state is None or column_name is None or column_name == "":
         return None
 
@@ -40,7 +43,7 @@ def generate_plot(df_state, column_name):
         # Reset Matplotlib figure for a clean slate
         plt.figure(figsize=(8, 5))
         
-        # Use a Histogram for distribution of the selected column
+        # Use a Histogram for distribution of the selected numeric column
         sns.histplot(df_state[column_name], kde=True)
         
         plt.title(f'Distribution of {column_name}')
@@ -66,6 +69,7 @@ with gr.Blocks(title="CSV Data Analyzer") as demo:
     gr.Markdown("Upload your CSV file and see instant statistics and visualizations.")
 
     # State component to hold the DataFrame across function calls
+    # This is essential for passing the DataFrame from `analyze_csv` to `generate_plot`
     df_state = gr.State(None)
 
     # Input components
@@ -73,7 +77,8 @@ with gr.Blocks(title="CSV Data Analyzer") as demo:
     
     with gr.Row():
         # Outputs
-        df_output = gr.Dataframe(label="Uploaded Data Preview", height=200, interactive=False)
+        # **FIXED:** Removed the unsupported 'height=200' argument.
+        df_output = gr.Dataframe(label="Uploaded Data Preview", interactive=False)
         stats_output = gr.Markdown(label="Descriptive Statistics")
     
     gr.HTML("<hr>")
@@ -89,14 +94,15 @@ with gr.Blocks(title="CSV Data Analyzer") as demo:
 
     # --- 4. Event Handling (Interactions) ---
     
-    # When a file is uploaded, run the analyze_csv function
+    # Triggered when a file is uploaded (or cleared)
     csv_file.upload(
         analyze_csv, 
         inputs=[csv_file], 
+        # Note: The output for the dropdown component is updated with new choices here.
         outputs=[df_output, stats_output, column_dropdown, df_state]
     )
 
-    # When the plot button is clicked, run the generate_plot function
+    # Triggered when the plot button is clicked
     plot_button.click(
         generate_plot, 
         inputs=[df_state, column_dropdown],