Update app.py

app.py

@@ -207,6 +207,141 @@ def plot_model_results(results_df, average_value, title, model_type):
         logger.error("Error plotting model results: %s", e)
         raise
 
+
+
+
+def plot_model(results_df, average_value, title, model_type):
+    """
+    Plot model results with specific orders and colors for Trust and NPS models.
+    Args:
+        results_df (DataFrame): DataFrame containing predictor names and their importance.
+        average_value (float): Average importance value.
+        title (str): Title of the plot.
+        model_type (str): Type of model (either "Trust" or "NPS").
+    Returns:
+        Image: Image object containing the plot.
+    """
+
+    logger.info(
+        "Plotting model results for %s model with title '%s'.", model_type, title
+    )
+    try:
+        # Define color scheme
+        color_map = {
+            "Stability": "#375570",
+            "Development": "#E3B05B",
+            "Relationship": "#C63F48",
+            "Benefit": "#418387",
+            "Vision": "#DF8859",
+            "Competence": "#6D93AB",
+            "Trust": "#f5918a",
+        }
+
+        # Define the order for each model
+        if model_type == "Trust":
+            order = [
+                "Stability",
+                "Development",
+                "Relationship",
+                "Benefit",
+                "Vision",
+                "Competence",
+            ]
+        else:  # "NPS"
+            order = [
+                "Trust",
+                "Stability",
+                "Development",
+                "Relationship",
+                "Benefit",
+                "Vision",
+                "Competence",
+            ]
+
+        # Apply the categorical ordering to the 'Predictor' column
+        results_df["Predictor"] = pd.Categorical(
+            results_df["Predictor"], categories=order, ordered=True
+        )
+        results_df.sort_values("Predictor", ascending=False, inplace=True)
+
+        # Create the figure and axis
+        fig, ax = plt.subplots(figsize=(10, 8))
+
+        # Set the x-axis labels with "%" using FuncFormatter
+        formatter = FuncFormatter(lambda x, _: f"{x:.0f}%")
+        ax.xaxis.set_major_formatter(formatter)
+
+        # Determine the dynamic range of the X-axis
+        actual_min = results_df["Importance_percent"].min()
+        actual_max = results_df["Importance_percent"].max()
+
+        # Calculate the x-axis limits
+        half_range = max(average_value - actual_min, actual_max - average_value)
+        x_min = 0  # start from zero
+        x_max = actual_max + 5  # a bit beyond max
+        plt.xlim(x_min, x_max)
+
+        # Set the x-axis ticks at every 5% interval and add dotted lines
+        x_ticks = np.arange(
+            np.floor(x_min), np.ceil(x_max) + 5, 5
+        )  # Ensures complete coverage
+        ax.set_xticks(x_ticks)  # Set the ticks on the axis
+        for tick in x_ticks:
+            ax.axvline(
+                x=tick, color="grey", linestyle="--", linewidth=0.5, zorder=2
+            )  # Add dotted lines
+
+        # Create bars: all from 0 → value (left-to-right only)
+        for i, row in enumerate(results_df.itertuples(index=False)):
+            color = color_map[row.Predictor]
+
+            ax.barh(
+                row.Predictor,
+                row.Importance_percent,
+                left=0,
+                color=color,
+                edgecolor="white",
+                height=0.6,
+                zorder=3,
+            )
+
+            ax.text(
+                row.Importance_percent + 0.5,
+                i,
+                f"{row.Importance_percent:.1f}%",
+                va="center",
+                ha="left",
+                color="#8c8b8c",
+            )
+
+        # Draw the average line and set the title
+        ax.axvline(average_value, color="black", linewidth=1, linestyle="-", zorder=3)
+        plt.title(title, fontsize=14)
+
+        # Remove plot borders
+        ax.spines[["left", "top", "right"]].set_color("none")
+
+        # Change the colour of y-axis text
+        ax.tick_params(axis="y", colors="#8c8b8c", length=0)
+
+        # Send axes to background and tighten the layout
+        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
+
+    except Exception as e:
+        logger.error("Error plotting model results: %s", e)
+        raise
 def plot_bucket_fullness(driver_df, title):
     # Determine required trust buckets
     buckets = [
@@ -657,7 +792,7 @@ def analyze_excel_single(file_path):
             results_df_nps = pd.read_csv(csv_output_path_nps)
             results_df_nps["Importance_percent"] = results_df_nps["Importance"] * 100
             average_value_nps = results_df_nps["Importance_percent"].mean()
-            img_nps = plot_model_results(
+            img_nps = plot_model(
                 results_df_nps,
                 average_value_nps,
                 f"NPS Drivers: {file_name}",