15th deployment. Overhaul user interface. Add bucket fullness chart. Hide file upload widget.

app.py

@@ -151,6 +151,96 @@ def plot_model_results(results_df, average_value, title, model_type):
     return img
 
 
+def plot_bucket_fullness(driver_df, title):
+    # Determine required trust buckets
+    buckets = [
+        "Stability",
+        "Development",
+        "Relationship",
+        "Benefit",
+        "Vision",
+        "Competence",
+    ]
+
+    # Check if columns are present in df
+    missing_columns = [col for col in buckets if col not in driver_df.columns]
+
+    if missing_columns:
+        logging.warning(
+            f"The following columns are missing in driver_df: {missing_columns}"
+        )
+        return None
+    logging.info("All required columns are present in driver_df.")
+
+    color_map = {
+        "Stability": "#642b93",
+        "Development": "#e9e23e",
+        "Relationship": "#c52121",
+        "Benefit": "#049c54",
+        "Vision": "#f4672a",
+        "Competence": "#2e3094",
+    }
+
+    order = buckets
+
+    # Calculate the percentage of fullness for each column in buckets
+    results_df = (driver_df[buckets].mean()).reset_index()
+    results_df.columns = ["Trust_Bucket", "Fullness_of_Bucket"]
+    results_df["Trust_Bucket"] = pd.Categorical(
+        results_df["Trust_Bucket"], categories=order, ordered=True
+    )
+    results_df.sort_values("Trust_Bucket", inplace=True)
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+
+    # formatter = FuncFormatter(lambda x, _: f"{x:.0f}%")
+    # ax.yaxis.set_major_formatter(formatter)
+
+    ax.bar(
+        results_df["Trust_Bucket"],
+        results_df["Fullness_of_Bucket"],
+        color=[color_map[bucket] for bucket in results_df["Trust_Bucket"]],
+        edgecolor="white",
+        zorder=2,
+    )
+
+    # Adding the percentage values on top of the bars
+    for i, row in enumerate(results_df.itertuples(index=False, name=None)):
+        trust_bucket, fullness_of_bucket = row
+        ax.text(
+            i,
+            fullness_of_bucket + 0.5,  # slightly above the top of the bar
+            f"{fullness_of_bucket:.1f}",
+            ha="center",
+            va="bottom",
+            color="#8c8b8c",
+        )
+
+    y_max = results_df["Fullness_of_Bucket"].max() + 1
+    plt.ylim(0, y_max)
+    plt.ylabel("Fullness")
+    plt.title(title, fontsize=14)
+
+    ax.spines[["top", "right"]].set_color("none")
+
+    # Adding grey dotted lines along the y-axis labels
+    y_ticks = ax.get_yticks()
+    for y_tick in y_ticks:
+        ax.axhline(y=y_tick, color="grey", linestyle="--", linewidth=0.5, zorder=1)
+
+    ax.set_axisbelow(True)
+    plt.tight_layout()
+
+    # Save the figure to a bytes buffer and then to an image
+    img_data = io.BytesIO()
+    plt.savefig(img_data, format="png", facecolor=fig.get_facecolor(), edgecolor="none")
+    img_data.seek(0)
+    img = Image.open(img_data)
+    plt.close(fig)
+
+    return img
+
+
 def call_r_script(
     input_file,
     text_output_path,
@@ -252,9 +342,6 @@ def analyze_excel_single(file_path):
     # Check if Driver and Builder sheets are present in the dataset
     trustbuilder_present = "Builder" in excel_file.sheet_names
 
-    # elif ".csv" in file_path:
-    #     df = pd.read_csv(file_path)
-
     # Step 1: Check for missing columns and handle NPS column
     required_columns = [
         "Trust",
@@ -334,6 +421,14 @@ def analyze_excel_single(file_path):
     with open(text_output_path, "r") as file:
         output_text = file.read()
 
+    # Get file name for display
+    file_name = file_path.split("/")[1]
+
+    # plot how full the trust buckets are
+    title = f"Trust Profile: {file_name}"
+    img_bucketfull = plot_bucket_fullness(df, title)
+
+    # plot trust
     # Get n_samples from output text
     n_samples_trust = output_text.split(": Trust")[1]
     n_samples_trust = n_samples_trust.split("Analysis based on ")[1]
@@ -346,10 +441,12 @@ def analyze_excel_single(file_path):
     img_trust = plot_model_results(
         results_df_trust,
         average_value_trust,
-        f"TrustLogic® Drivers\n(What drives your trust)\nn={n_samples_trust}",
+        # f"TrustLogic® Drivers\n(What drives your trust)\nn={n_samples_trust}",
+        f"Trust Drivers: {file_name}",
         "Trust",
     )
 
+    # plot NPS
     img_nps = None
     if nps_present:
         # Get n_samples from output text
@@ -363,10 +460,12 @@ def analyze_excel_single(file_path):
         img_nps = plot_model_results(
             results_df_nps,
             average_value_nps,
-            f"TrustLogic® Drivers\n(What drives your NPS)\nn={n_samples_nps}",
+            # f"TrustLogic® Drivers\n(What drives your NPS)\nn={n_samples_nps}",
+            f"NPS Drivers: {file_name}",
             "NPS",
         )
 
+    # plot loyalty
     img_loyalty = None
     if loyalty_present:
         # Get n_samples from output text
@@ -382,10 +481,12 @@ def analyze_excel_single(file_path):
         img_loyalty = plot_model_results(
             results_df_loyalty,
             average_value_loyalty,
-            f"TrustLogic® Drivers\n(What drives your Loyalty)\nn={n_samples_loyalty}",
+            # f"TrustLogic® Drivers\n(What drives your Loyalty)\nn={n_samples_loyalty}",
+            f"Loyalty Drivers: {file_name}",
             "Loyalty",
         )
 
+    # plot consideration
     img_consideration = None
     if consideration_present:
         # Get n_samples from output text
@@ -403,10 +504,12 @@ def analyze_excel_single(file_path):
         img_consideration = plot_model_results(
             results_df_consideration,
             average_value_consideration,
-            f"TrustLogic® Drivers\n(What drives your Consideration)\nn={n_samples_consideration}",
+            # f"TrustLogic® Drivers\n(What drives your Consideration)\nn={n_samples_consideration}",
+            f"Consideration Drivers: {file_name}",
             "Consideration",
         )
 
+    # plot satisfaction
     img_satisfaction = None
     if satisfaction_present:
         # Get n_samples from output text
@@ -424,10 +527,12 @@ def analyze_excel_single(file_path):
         img_satisfaction = plot_model_results(
             results_df_satisfaction,
             average_value_satisfaction,
-            f"TrustLogic® Drivers\n(What drives your Satisfaction)\nn={n_samples_satisfaction}",
+            # f"TrustLogic® Drivers\n(What drives your Satisfaction)\nn={n_samples_satisfaction}",
+            f"Satisfaction Drivers: {file_name}",
             "Satisfaction",
         )
 
+    # plot trust builder table 1 and 2
     df_builder = None
     df_builder_pivot = None
     if trustbuilder_present:
@@ -541,6 +646,7 @@ def analyze_excel_single(file_path):
         )
 
     return (
+        img_bucketfull,
         img_trust,
         img_nps,
         img_loyalty,
@@ -569,6 +675,7 @@ def batch_file_processing(file_paths):
     """
 
     # Process each file
+    img_bucketfull_list = []
     img_trust_list = []
     img_nps_list = []
     img_loyalty_list = []
@@ -580,6 +687,7 @@ def batch_file_processing(file_paths):
 
     for file_path in file_paths:
         (
+            img_bucketfull,
             img_trust,
             img_nps,
             img_loyalty,
@@ -589,6 +697,7 @@ def batch_file_processing(file_paths):
             df_builder_pivot,
             output_text,
         ) = analyze_excel_single(file_path)
+        img_bucketfull_list.append(img_bucketfull)
         img_trust_list.append(img_trust)
         img_nps_list.append(img_nps)
         img_loyalty_list.append(img_loyalty)
@@ -599,6 +708,7 @@ def batch_file_processing(file_paths):
         output_text_list.append(output_text)
 
     return (
+        img_bucketfull_list,
         img_trust_list,
         img_nps_list,
         img_loyalty_list,
@@ -631,6 +741,7 @@ def variable_outputs(file_inputs):
 
     # Call batch file processing and get analysis results
     (
+        img_bucketfull_list,
         img_trust_list,
         img_nps_list,
         img_loyalty_list,
@@ -649,6 +760,7 @@ def variable_outputs(file_inputs):
 
     # Use zip_longest to iterate over the lists, padding with None
     for row, (
+        img_bucketfull,
         img_trust,
         img_nps,
         img_loyalty,
@@ -659,6 +771,7 @@ def variable_outputs(file_inputs):
         output_text,
     ) in enumerate(
         zip_longest(
+            img_bucketfull_list,
             img_trust_list,
             img_nps_list,
             img_loyalty_list,
@@ -674,6 +787,29 @@ def variable_outputs(file_inputs):
 
         # Based on the number of files uploaded, determine the content of each textbox
         plots = [
+            gr.Markdown(
+                "<span style='font-size:20px; font-weight:bold;'>1) Trust Profile</span>"
+            ),
+            gr.Markdown(
+                "This analysis shows you show strongly you are trusted in each of the six Trust Buckets®. You can also see this for any competitor."
+            ),
+            gr.Image(
+                value=img_bucketfull,
+                type="pil",
+                # label=f"{dataset_name}: Trust Drivers",
+                label="Trust Profile",
+                visible=True,
+            ),
+            gr.Markdown(
+                "<span style='font-size:20px; font-weight:bold;'>2) Trust and KPI Drivers</span>"
+            ),
+            gr.Markdown(
+                "This analysis shows you which of the TrustLogic® dimensions are most effective in building more trust and improving your KPIs. "
+                + "Here we display Trust and NPS, but in the full version you can include up to four KPIs (e.g. CSAT, Consideration, Loyalty). ​"
+                + "<br>The Trust Buckets® extending to the right are the more important ones. We show how they over and under-index. "
+                + "The average driver impact is 16.7% (100% divided by 6 trust dimensions). The higher the % above average, the more important. "
+                + "That means that you need to ‘fill’ these Trust Buckets® with the right attributes and messages."
+            ),
             gr.Image(
                 value=img_trust,
                 type="pil",
@@ -688,30 +824,50 @@ def variable_outputs(file_inputs):
                 label="NPS Drivers",
                 visible=True,
             ),
-            # gr.Image(
-            #     value=img_loyalty,
-            #     type="pil",
-            #     #label=f"{dataset_name}: Loyalty Drivers",
-            #     visible=True,
-            # ),
-            # gr.Image(
-            #     value=img_consideration,
-            #     type="pil",
-            #     #label=f"{dataset_name}: Consideration Drivers",
-            #     visible=True,
-            # ),
-            # gr.Image(
-            #     value=img_satisfaction,
-            #     type="pil",
-            #     #label=f"{dataset_name}: Satisfaction Drivers",
-            #     visible=True,
-            # ),
-            # gr.Textbox(
-            #     value=output_text,
-            #     #label=f"{dataset_name}: Analysis Summary",
-            #     visible=False,
-            # ),
-            gr.Dataframe(
+            gr.Image(
+                value=img_loyalty,
+                type="pil",
+                # label=f"{dataset_name}: Loyalty Drivers",
+                visible=True,
+            ),
+            gr.Image(
+                value=img_consideration,
+                type="pil",
+                # label=f"{dataset_name}: Consideration Drivers",
+                visible=True,
+            ),
+            gr.Image(
+                value=img_satisfaction,
+                type="pil",
+                # label=f"{dataset_name}: Satisfaction Drivers",
+                visible=True,
+            ),
+            gr.Textbox(
+                value=output_text,
+                # label=f"{dataset_name}: Analysis Summary",
+                visible=False,
+            ),
+        ]
+
+        # add current plots to container
+        plots_visible += plots
+
+        if isinstance(df_builder, pd.DataFrame) and isinstance(
+            df_builder_pivot, pd.DataFrame
+        ):
+            logging.debug(f"df_builder: {df_builder}")
+            logging.debug(f"df_builder_pivot: {df_builder_pivot}")
+
+            markdown_5 = gr.Markdown(
+                "<span style='font-size:20px; font-weight:bold;'>3) Proof Points</span>"
+            )
+            markdown_6 = gr.Markdown(
+                "These are the reasons to trust and recommend. They can be your brand values, features, attributes, programmes and messages. "
+                + "<br>In the first table, use the little arrow in each column to toggle the most to least effective proof points to fill each Trust Bucket®. Your focus is only on the Trust Bucket® with the highest driver impact. "
+                + "<br>In the second table you see the top scoring proof points ordered by Trust Bucket®."
+            )
+
+            table_builder_1 = gr.Dataframe(
                 value=df_builder,
                 headers=list(df_builder.columns),
                 interactive=False,
@@ -719,8 +875,8 @@ def variable_outputs(file_inputs):
                 visible=True,
                 height=800,
                 wrap=True,
-            ),
-            gr.Dataframe(
+            )
+            table_builder_2 = gr.Dataframe(
                 value=df_builder_pivot,
                 headers=list(df_builder_pivot.columns),
                 interactive=False,
@@ -728,23 +884,38 @@ def variable_outputs(file_inputs):
                 visible=True,
                 height=800,
                 wrap=True,
-            ),
-        ]
+            )
 
-        # add current plots to container
-        plots_visible += plots
+            # add builder tables to container
+            plots_visible.append(markdown_5)
+            plots_visible.append(markdown_6)
+            plots_visible.append(table_builder_1)
+            plots_visible.append(table_builder_2)
+        else:
+            # otherwise, add invisible tables
+            empty_markdown = gr.Markdown("", visible=False)
+            empty_table = gr.Dataframe(value=None, label=" ", visible=False)
+            plots_visible.append(empty_markdown)
+            plots_visible.append(empty_markdown)
+            plots_visible.append(empty_table)
+            plots_visible.append(empty_table)
 
     plots_invisible = [
-        # gr.Image(label="Trust Drivers", visible=False),
-        # gr.Image(label="NPS Drivers", visible=False),
-        # gr.Image(label="Loyalty Drivers", visible=False),
-        # gr.Image(label="Consideration Drivers", visible=False),
-        # gr.Image(label="Satisfaction Drivers", visible=False),
-        # gr.Textbox(label="Analysis Summary", visible=False),
+        gr.Markdown("", visible=False),
+        gr.Markdown("", visible=False),
+        gr.Image(label="Trust Buckets", visible=False),
+        gr.Markdown("", visible=False),
+        gr.Markdown("", visible=False),
         gr.Image(label="Trust Drivers", visible=False),
         gr.Image(label="NPS Drivers", visible=False),
-        gr.Dataframe(label=" ", visible=False),
-        gr.Dataframe(label=" ", visible=False),
+        gr.Image(label="Loyalty Drivers", visible=False),
+        gr.Image(label="Consideration Drivers", visible=False),
+        gr.Image(label="Satisfaction Drivers", visible=False),
+        gr.Textbox(label="Analysis Summary", visible=False),
+        gr.Markdown("", visible=False),
+        gr.Markdown("", visible=False),
+        gr.Dataframe(value=None, label=" ", visible=False),
+        gr.Dataframe(value=None, label=" ", visible=False),
     ]
 
     return plots_visible + plots_invisible * (max_outputs - k)
@@ -755,55 +926,83 @@ def reset_outputs():
     outputs = []
 
     # Create fixed dummy components
-    # trust_plot = gr.Image(value=None, label="Trust Drivers", visible=True)
-    # nps_plot = gr.Image(value=None, label="NPS Drivers", visible=True)
-    # loyalty_plot = gr.Image(value=None, label="Loyalty Drivers", visible=True)
-    # consideration_plot = gr.Image(
-    #     value=None, label="Consideration Drivers", visible=True
-    # )
-    # satisfaction_plot = gr.Image(
-    #     value=None, label="Satisfaction Drivers", visible=True
-    # )
-    # summary_text = gr.Textbox(value=None, label="Analysis Summary", visible=False)
+    markdown_1 = gr.Markdown(
+        "<span style='font-size:20px; font-weight:bold;'>1) Trust Profile</span>"
+    )
+    markdown_2 = gr.Markdown(
+        "This analysis shows you show strongly you are trusted in each of the six Trust Buckets®. You can also see this for any competitor."
+    )
+    buckets_plot = gr.Image(value=None, label="Trust Buckets", visible=True)
+
+    markdown_3 = gr.Markdown(
+        "<span style='font-size:20px; font-weight:bold;'>2) Trust and KPI Drivers</span>"
+    )
+    markdown_4 = gr.Markdown(
+        "This analysis shows you which of the TrustLogic® dimensions are most effective in building more trust and improving your KPIs. "
+        + "Here we display Trust and NPS, but in the full version you can include up to four KPIs (e.g. CSAT, Consideration, Loyalty). ​"
+        + "<br>The Trust Buckets® extending to the right are the more important ones. We show how they over and under-index. "
+        + "The average driver impact is 16.7% (100% divided by 6 trust dimensions). The higher the % above average, the more important. "
+        + "That means that you need to ‘fill’ these Trust Buckets® with the right attributes and messages."
+    )
     trust_plot = gr.Image(value=None, label="Trust Drivers", visible=True)
     nps_plot = gr.Image(value=None, label="NPS Drivers", visible=True)
+    loyalty_plot = gr.Image(value=None, label="Loyalty Drivers", visible=True)
+    consideration_plot = gr.Image(
+        value=None, label="Consideration Drivers", visible=True
+    )
+    satisfaction_plot = gr.Image(value=None, label="Satisfaction Drivers", visible=True)
+    summary_text = gr.Textbox(value=None, label="Analysis Summary", visible=False)
+
+    markdown_5 = gr.Markdown(
+        "<span style='font-size:20px; font-weight:bold;'>3) Proof Points</span>"
+    )
+    markdown_6 = gr.Markdown(
+        "These are the reasons to trust and recommend. They can be your brand values, features, attributes, programmes and messages. "
+        + "<br>In the first table, use the little arrow in each column to toggle the most to least effective proof points to fill each Trust Bucket®. Your focus is only on the Trust Bucket® with the highest driver impact. "
+        + "<br>In the second table you see the top scoring proof points ordered by Trust Bucket®."
+    )
+
     df_builder = gr.Dataframe(value=None, label=" ", visible=True)
     df_builder_pivot = gr.Dataframe(value=None, label=" ", visible=True)
+
+    outputs.append(markdown_1)
+    outputs.append(markdown_2)
+    outputs.append(buckets_plot)
+    outputs.append(markdown_3)
+    outputs.append(markdown_4)
     outputs.append(trust_plot)
     outputs.append(nps_plot)
+    outputs.append(loyalty_plot)
+    outputs.append(consideration_plot)
+    outputs.append(satisfaction_plot)
+    outputs.append(summary_text)
+    outputs.append(markdown_5)
+    outputs.append(markdown_6)
     outputs.append(df_builder)
     outputs.append(df_builder_pivot)
-    # outputs.append(loyalty_plot)
-    # outputs.append(satisfaction_plot)
-    # outputs.append(consideration_plot)
-    # outputs.append(summary_text)
 
     # invisible from second set onwards
     for i in range(1, max_outputs):
-        # trust_plot = gr.Image(value=None, label="Trust Drivers", visible=False)
-        # nps_plot = gr.Image(value=None, label="NPS Drivers", visible=False)
-        # loyalty_plot = gr.Image(
-        #     value=None, label="Loyalty Drivers", visible=False
-        # )
-        # consideration_plot = gr.Image(
-        #     value=None, label="Consideration Drivers", visible=False
-        # )
-        # satisfaction_plot = gr.Image(
-        #     value=None, label="Satisfaction Drivers", visible=False
-        # )
-        # summary_text = gr.Textbox(value=None, label="Analysis Summary", visible=False)
-        trust_plot = gr.Image(value=None, label="Trust Drivers", visible=False)
-        nps_plot = gr.Image(value=None, label="NPS Drivers", visible=False)
-        df_builder = gr.Dataframe(value=None, label=" ", visible=False)
-        df_builder_pivot = gr.Dataframe(value=None, label=" ", visible=False)
-        outputs.append(trust_plot)
-        outputs.append(nps_plot)
-        outputs.append(df_builder)
-        outputs.append(df_builder_pivot)
-        # outputs.append(loyalty_plot)
-        # outputs.append(consideration_plot)
-        # outputs.append(satisfaction_plot)
-        # outputs.append(summary_text)
+        markdown_empty = gr.Markdown("", visible=False)
+        plot_empty = gr.Image(value=None, label="", visible=False)
+        df_empty = gr.Dataframe(value=None, label=" ", visible=False)
+        text_empty = gr.Textbox(value=None, label="", visible=False)
+
+        outputs.append(markdown_empty)
+        outputs.append(markdown_empty)
+        outputs.append(plot_empty)
+        outputs.append(markdown_empty)
+        outputs.append(markdown_empty)
+        outputs.append(plot_empty)
+        outputs.append(plot_empty)
+        outputs.append(plot_empty)
+        outputs.append(plot_empty)
+        outputs.append(plot_empty)
+        outputs.append(text_empty)
+        outputs.append(markdown_empty)
+        outputs.append(markdown_empty)
+        outputs.append(df_empty)
+        outputs.append(df_empty)
 
     return outputs
 
@@ -812,6 +1011,7 @@ def process_examples(file_name):
     file_path = f"example_files/{file_name[0]}"
     file_path = [file_path]
     outputs = variable_outputs(file_path)
+
     return outputs
 
 
@@ -862,50 +1062,144 @@ def process_examples(file_name):
 #         dataset.click(fn=process_examples, inputs=dataset, outputs=outputs)
 
 with gr.Blocks() as demo:
-    # with gr.Column():
-    #     title = gr.Markdown("# TrustLogic & NPS Driver Analysis (Batch Version)")
-    #     description = gr.Markdown(instruction_text)
+    with gr.Column():
+        #     title = gr.Markdown("# TrustLogic & NPS Driver Analysis (Batch Version)")
+        #     description = gr.Markdown(instruction_text)
+        gr.Markdown(
+            "<span style='font-size:20px; font-weight:bold;'>Click 'Volkswagen Customers' or 'Volkswagen Prospects' to see the full results and play with the TrustAI.</span>"
+        )
+        gr.Markdown(
+            "Our calculator will conduct the driver analysis from the underlying Excel file and display the results. "
+            + "Scroll down to view them and interact with them. "
+            + "In the full version you can link your survey directly to our calculator or export your data as CSV and drag & drop it into our calculator."
+        )
 
     with gr.Column():
         with gr.Row():
-            dataset = gr.Dataset(
-                components=[gr.Textbox(visible=False)],
-                label="Select an example to calculate the Donor Drivers from the underlying Excel files",
-                type="values",
-                samples=[
-                    ["VW.xlsx"],
-                    # ["HSBC.xlsx"],
-                    # ["CBA.xlsx"],
-                    # ["Red Cross.xlsx"],
-                    # ["Health Insurance.xlsx"],
-                    # ["WV.xlsx"],
-                    # ["Care.xlsx"],
-                    # ["BUPA.xlsx"],
-                ],
-            )
+            # dataset_1 = gr.Dataset(
+            #     components=[gr.Textbox(visible=False)],
+            #     # label="Select an example to calculate the Trust Drivers from the underlying Excel files",
+            #     label="",
+            #     type="values",
+            #     samples=[
+            #         ["Volkswagen Customers.xlsx"],
+            #         ["Volkswagen Prospects.xlsx"],
+            #     ],
+            # )
+            vw_customers_btn = gr.Button("Volkswagen Customers")
+            vw_prospects_btn = gr.Button("Volkswagen Prospects")
 
         with gr.Row():
-            # set file upload widget
-            file_inputs = gr.Files(label="Excel Dataset")
+            gr.Markdown(
+                "<span style='font-size:20px; font-weight:bold;'>Click any of the examples below to see top-line driver results in different categories.</span>"
+            )
 
         with gr.Row():
-            # set clear and submit butttons
-            clear_button = gr.ClearButton(file_inputs)
-            submit_button = gr.Button("Submit", variant="primary")
+            # dataset_2 = gr.Dataset(
+            #     components=[gr.Textbox(visible=False)],
+            #     # label="Select an example to calculate the Trust Drivers from the underlying Excel files",
+            #     label="",
+            #     type="values",
+            #     samples=[
+            #         ["HSBC.xlsx"],
+            #         ["CBA.xlsx"],
+            #         ["BUPA.xlsx"],
+            #         ["Health Insurance.xlsx"],
+            #         ["Care.xlsx"],
+            #         ["WV.xlsx"],
+            #         ["Red Cross.xlsx"],
+            #     ],
+            # )
+
+            hsbc_btn = gr.Button("HSBC")
+            cba_btn = gr.Button("CBA")
+            bupa_btn = gr.Button("BUPA")
+            health_insurance_btn = gr.Button("Health Insurance")
+            care_btn = gr.Button("Care")
+            wv_btn = gr.Button("WV")
+            red_cross_btn = gr.Button("Red Cross")
+
+        # with gr.Row():
+        #     # set file upload widget
+        #     file_inputs = gr.Files(label="Excel Dataset")
+
+        # with gr.Row():
+        #     # set clear and submit butttons
+        #     clear_button = gr.ClearButton(file_inputs)
+        #     submit_button = gr.Button("Submit", variant="primary")
 
         with gr.Column():
             # set default output widgets
             outputs = reset_outputs()
 
-        # function for submit button click
-        submit_button.click(fn=variable_outputs, inputs=file_inputs, outputs=outputs)
+        # # function for submit button click
+        # submit_button.click(fn=variable_outputs, inputs=file_inputs, outputs=outputs)
 
-        # function for clear button click
-        # this only handles the outputs. Input reset is handled at button definition
-        clear_button.click(fn=reset_outputs, inputs=[], outputs=outputs)
+        # # function for clear button click
+        # # this only handles the outputs. Input reset is handled at button definition
+        # clear_button.click(fn=reset_outputs, inputs=[], outputs=outputs)
 
         # function for example files
-        dataset.click(fn=process_examples, inputs=dataset, outputs=outputs)
+        # dataset.click(fn=process_examples, inputs=dataset, outputs=outputs)
+        # dataset_1.click(fn=process_examples, inputs=dataset_1, outputs=outputs)
+        # dataset_2.click(fn=process_examples, inputs=dataset_2, outputs=outputs)
+
+        # Create gr.State components to store file names as lists
+        vw_customers_state = gr.State(value=["Volkswagen Customers.xlsx"])
+        vw_prospects_state = gr.State(value=["Volkswagen Prospects.xlsx"])
+        hsbc_state = gr.State(value=["HSBC.xlsx"])
+        cba_state = gr.State(value=["CBA.xlsx"])
+        bupa_state = gr.State(value=["BUPA.xlsx"])
+        health_insurance_state = gr.State(value=["Health Insurance.xlsx"])
+        care_state = gr.State(value=["Care.xlsx"])
+        wv_state = gr.State(value=["WV.xlsx"])
+        red_cross_state = gr.State(value=["Red Cross.xlsx"])
+
+        vw_customers_btn.click(
+            fn=process_examples,
+            inputs=[vw_customers_state],
+            outputs=outputs,
+        )
+        vw_prospects_btn.click(
+            fn=process_examples,
+            inputs=[vw_prospects_state],
+            outputs=outputs,
+        )
+        hsbc_btn.click(
+            fn=process_examples,
+            inputs=[hsbc_state],
+            outputs=outputs,
+        )
+        cba_btn.click(
+            fn=process_examples,
+            inputs=[cba_state],
+            outputs=outputs,
+        )
+        bupa_btn.click(
+            fn=process_examples,
+            inputs=[bupa_state],
+            outputs=outputs,
+        )
+        health_insurance_btn.click(
+            fn=process_examples,
+            inputs=[health_insurance_state],
+            outputs=outputs,
+        )
+        care_btn.click(
+            fn=process_examples,
+            inputs=[care_state],
+            outputs=outputs,
+        )
+        wv_btn.click(
+            fn=process_examples,
+            inputs=[wv_state],
+            outputs=outputs,
+        )
+        red_cross_btn.click(
+            fn=process_examples,
+            inputs=[red_cross_state],
+            outputs=outputs,
+        )
 
 
 demo.launch(server_name="0.0.0.0")