Update app.py

app.py

@@ -74,6 +74,120 @@ df_builder_pivot_str = ""
 
 
 
+
+
+
+def plot_model_results(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:
+        import math
+
+        # Color mapping
+        color_map = {
+            "Stability": "#375570",
+            "Development": "#E3B05B",
+            "Relationship": "#C63F48",
+            "Benefit": "#418387",
+            "Vision": "#DF8859",
+            "Competence": "#6D93AB",
+            "Trust": "#f5918a",
+        }
+
+        # Load Trust Core Image
+        image_path = "./images/image.png"
+        try:
+            trust_core_img = Image.open(image_path)
+        except FileNotFoundError:
+            raise FileNotFoundError(f"❌ Error: Trust Core image '{image_path}' not found!")
+
+            # 🟠 NORMAL BAR PLOT
+            # 🟢 BUBBLE PLOT for NPS (copied exactly from your plot_trust_driver_bubbles)
+        order = ["Trust", "Stability", "Development", "Relationship", "Benefit", "Vision", "Competence"]
+
+        results_df["Predictor"] = pd.Categorical(
+            results_df["Predictor"], categories=order, ordered=True
+        )
+        results_df.sort_values("Predictor", ascending=False, inplace=True)
+
+        # Extract values
+        values_dict = results_df.set_index("Predictor")["Importance_percent"].to_dict()
+        percentages = [values_dict.get(pred, 0) for pred in order]
+
+        # Bubble sizes
+        min_radius = 0.15
+        base_percentage = min(percentages) if min(percentages) > 0 else 1
+        bubble_radii = [min_radius * (p / base_percentage) ** 0.75 for p in percentages]
+
+        # Central core
+        central_radius = 0.8
+
+        # Bubble positions
+        default_positions = {
+            "Trust": (0.0, 1.3),
+            "Stability": (-1.05, 0.0),
+            "Development": (1.05, 0.0),
+            "Relationship": (-0.6, 0.85),
+            "Benefit": (0.6, -0.85),
+            "Vision": (0.6, 0.85),
+            "Competence": (-0.6, -0.85)
+        }
+
+        # Adjust bubble positions slightly to touch core
+        for i, predictor in enumerate(order):
+            x, y = default_positions[predictor]
+            r = bubble_radii[i]
+            distance = np.sqrt(x**2 + y**2)
+            scale_factor = (central_radius + r - 0.2) / distance
+            default_positions[predictor] = (x * scale_factor, y * scale_factor)
+
+        fig, ax = plt.subplots(figsize=(10, 10), dpi=300)
+        ax.set_xlim(-2, 2)
+        ax.set_ylim(-2, 2)
+        ax.set_aspect('equal')
+        ax.axis("off")
+
+        # Draw Trust Core
+        extent = [-central_radius, central_radius, -central_radius, central_radius]
+        ax.imshow(trust_core_img, extent=extent, alpha=1.0)
+
+        # Draw Bubbles
+        for i, predictor in enumerate(order):
+            x, y = default_positions[predictor]
+            r = bubble_radii[i]
+            color = color_map.get(predictor, "#cccccc")
+            circle = patches.Circle((x, y), r, facecolor=color, alpha=1.0, lw=1.5)
+            ax.add_patch(circle)
+            ax.text(x, y, f"{percentages[i]:.1f}%", fontsize=10, fontweight="bold", ha="center", va="center", color="white")
+
+        plt.title(title, fontsize=12)
+
+        # Save and return image
+        img_data = io.BytesIO()
+        plt.savefig(img_data, format="png", bbox_inches="tight", facecolor=fig.get_facecolor())
+        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_model_results(results_df, average_value, title, model_type):
     """
     Plot model results with specific orders and colors for Trust and NPS models.
@@ -663,6 +777,7 @@ def analyze_excel_single(file_path):
             average_value_nps = results_df_nps["Importance_percent"].mean()
             img_nps = plot_model_results(
                 results_df_nps,
+                average_value_nps,
                 f"NPS Drivers: {file_name}",
                 "NPS",
             )
@@ -1039,6 +1154,18 @@ def variable_outputs(file_inputs):
             "<span style='font-size:20px; font-weight:bold;'>3) Trust and KPI Drivers</span>",
             visible=True,
         ),
+        gr.Markdown(
+            """
+            <div style='font-size:16px;'>
+            This analysis highlights which Trust Buckets® are most effective in improving NPS and building trust.
+            <br><br>
+            The baseline impact for each driver is <b>16.7%</b> (100% divided across 6 Trust Buckets®). Any percentage above this average indicates higher significance,
+            meaning these Trust Buckets® require more attention. To maximise their potential, focus on “filling” them with the right attributes and tailored messaging.
+            </div>
+            """,
+            visible=True,
+        ),
+    
         
         gr.Image(
             value=img_trust,