Update app.py

app.py

@@ -4,69 +4,112 @@ import matplotlib.pyplot as plt
 from fpdf import FPDF
 import io
 
+# Global DataFrame to store the CSV data
 df = pd.DataFrame()
 
 def upload_csv(file):
     global df
     try:
+        # Check if a file was actually uploaded
+        if file is None:
+            return (["All"], ["All"]), "No file uploaded. Please upload a CSV file."
+        
+        # Read the CSV file
         df = pd.read_csv(file.name)
+        
+        # Check if the DataFrame is empty
+        if df.empty:
+            return (["All"], ["All"]), "The uploaded CSV file is empty."
+        
+        # Define required columns
         required_columns = {'DeviceID', 'Lab', 'Type', 'Timestamp', 'Status', 'UsageCount'}
+        
+        # Check if all required columns are present
         if not required_columns.issubset(df.columns):
-            return gr.update(choices=["All"]), gr.update(choices=["All"]), "❌ CSV must contain columns: " + ", ".join(required_columns)
+            missing_cols = required_columns - set(df.columns)
+            return (["All"], ["All"]), f"CSV is missing required columns: {', '.join(missing_cols)}"
+        
+        # Extract unique values for dropdowns
         labs = ['All'] + sorted(df['Lab'].dropna().unique().tolist())
         types = ['All'] + sorted(df['Type'].dropna().unique().tolist())
-        return gr.update(choices=labs, value="All"), gr.update(choices=types, value="All"), ""
+        
+        return (labs, types), ""
     except Exception as e:
-        return gr.update(choices=["All"]), gr.update(choices=["All"]), f"❌ Failed to load CSV: {str(e)}"
+        # Handle any other errors (e.g., file not readable, invalid CSV format)
+        return (["All"], ["All"]), f"Failed to load CSV: {str(e)}"
 
 def filter_and_plot(selected_lab, selected_type):
     global df
+    # If no data is loaded, return None (no plot)
     if df.empty:
         return None
+    
+    # Create a copy of the DataFrame for filtering
     filtered_df = df.copy()
+    
+    # Apply filters based on dropdown selections
     if selected_lab != "All":
         filtered_df = filtered_df[filtered_df["Lab"] == selected_lab]
     if selected_type != "All":
         filtered_df = filtered_df[filtered_df["Type"] == selected_type]
     
-    # Plotting
+    # If no data remains after filtering, return None
+    if filtered_df.empty:
+        return None
+    
+    # Prepare the plot
     plt.figure(figsize=(8, 4))
     status_counts = filtered_df["Status"].value_counts()
     status_counts.plot(kind="bar", color=["green", "red"])
-    plt.title("Device Status Overview")
+    plt.title("Status Counts")
     plt.xlabel("Status")
     plt.ylabel("Count")
     
+    # Save the plot to a buffer
     buf = io.BytesIO()
     plt.savefig(buf, format="png")
+    plt.close()  # Close the plot to free memory
     buf.seek(0)
     return buf
 
 def download_pdf(selected_lab, selected_type):
     global df
+    # If no data is loaded, return None (no PDF)
     if df.empty:
         return None
+    
+    # Create a copy of the DataFrame for filtering
     filtered_df = df.copy()
+    
+    # Apply filters based on dropdown selections
     if selected_lab != "All":
         filtered_df = filtered_df[filtered_df["Lab"] == selected_lab]
     if selected_type != "All":
         filtered_df = filtered_df[filtered_df["Type"] == selected_type]
-
+    
+    # If no data remains after filtering, return None
+    if filtered_df.empty:
+        return None
+    
+    # Generate the PDF
     pdf = FPDF()
     pdf.add_page()
     pdf.set_font("Arial", size=12)
     pdf.cell(200, 10, txt="LabOps Summary Report", ln=True, align='C')
     pdf.ln(10)
-
+    
+    # Add data to the PDF
     for index, row in filtered_df.iterrows():
         line = f"{row['Timestamp']} | {row['DeviceID']} | {row['Lab']} | {row['Type']} | {row['Status']} | {row['UsageCount']}"
         pdf.multi_cell(0, 10, txt=line)
-
+    
+    # Save the PDF to a buffer
     output = io.BytesIO()
     pdf.output(output)
     output.seek(0)
     return output
 
+# Build the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("🧪 **LabOps Dashboard with Filters**\nUpload lab device logs, filter by Lab and Equipment Type, visualize uptime/downtime & generate PDF reports.")
 
@@ -77,15 +120,14 @@ with gr.Blocks() as demo:
         lab_dropdown = gr.Dropdown(label="Select Lab", choices=["All"], value="All")
         type_dropdown = gr.Dropdown(label="Select Equipment Type", choices=["All"], value="All")
 
-    error_box = gr.Textbox(label="Message", visible=True, interactive=False)
-
-    plot_output = gr.Image(label="Device Status Plot")
+    plot_output = gr.Image(label="Plot")
     download_btn = gr.Button("Download PDF Summary")
+    error_box = gr.Textbox(label="Status/Error Message", visible=True, interactive=False)
 
-    # Hook up event logic
-    csv_input.change(fn=upload_csv, inputs=csv_input, outputs=[lab_dropdown, type_dropdown, error_box])
+    # Connect the components
+    csv_input.change(fn=upload_csv, inputs=csv_input, outputs=[(lab_dropdown, type_dropdown), error_box])
     lab_dropdown.change(fn=filter_and_plot, inputs=[lab_dropdown, type_dropdown], outputs=plot_output)
     type_dropdown.change(fn=filter_and_plot, inputs=[lab_dropdown, type_dropdown], outputs=plot_output)
-    download_btn.click(fn=download_pdf, inputs=[lab_dropdown, type_dropdown], outputs=gr.File())
+    download_btn.click(fn=download_pdf, inputs=[lab_dropdown, type_dropdown], outputs=gr.File(label="Download PDF"))
 
-demo.launch()
+demo.launch()