Update app.py

app.py

@@ -437,6 +437,86 @@ def call_r_script(
         raise
 
 
+
+def plot_trust_driver_bubbles(trust_df, title):
+    """
+    Creates a bubble plot for Trust Drivers using preset positions and colors.
+    
+    Args:
+        trust_df (DataFrame): DataFrame containing Trust driver data with an "Importance_percent" column.
+        title (str): Title of the plot.
+    
+    Returns:
+        Image: PIL Image of the bubble plot.
+    """
+    # Set the background image path
+    image_path = "./images/image.png"
+
+    # Load background image
+    try:
+        img = Image.open(image_path)
+    except FileNotFoundError:
+        raise FileNotFoundError(f"❌ Error: Background image '{image_path}' not found!")
+
+    # Define the fixed bubble order (for Trust Drivers)
+    bubble_order = ["Vision", "Development", "Benefit", "Competence", "Stability", "Relationship"]
+
+    # Colors for each bubble (in the same order)
+    colors = ["#DF8859", "#E3B05B", "#418387", "#6D93AB", "#375570", "#C63F48"]
+
+    # Bubble positions (aligned with the background image)
+    bubble_positions = [
+        (0.66, 1.20),   # Vision (moved up)
+        (1.3, -0.1),   # Development
+        (0.66, -1.10),  # Benefit
+        (-0.70, -1.20), # Competence
+        (-1.42, 0.14),  # Stability
+        (-0.70, 1.15)   # Relationship (shifted left)
+    ]
+
+    # Extract importance percentages for each predictor.
+    # If a predictor is missing, default to 0.
+    values_dict = trust_df.set_index("Predictor")["Importance_percent"].to_dict()
+    percentages = [values_dict.get(pred, 0) for pred in bubble_order]
+
+    # Scale bubble sizes dynamically based on the percentages
+    max_size = 0.35  # Maximum bubble size
+    min_size = 0.23  # Minimum bubble size
+    min_value, max_value = min(percentages), max(percentages)
+    bubble_sizes = [
+        min_size + (max_size - min_size) * ((p - min_value) / (max_value - min_value + 1e-5))
+        for p in percentages
+    ]
+
+    # Create the figure and axis
+    fig, ax = plt.subplots(figsize=(8, 8))
+    ax.set_xlim(-2, 2)
+    ax.set_ylim(-2, 2)
+    ax.set_aspect('equal')  # Lock the aspect ratio
+    ax.axis("off")
+
+    # Display the background image (ensure the extent aligns with your coordinate system)
+    ax.imshow(img, extent=[-1.5, 1.5, -1.5, 1.5])
+
+    # Draw bubbles and add centered percentage text
+    for i, (x, y) in enumerate(bubble_positions):
+        size = bubble_sizes[i]
+        circle = patches.Circle((x, y), size, facecolor=colors[i], alpha=1.0, lw=1.5)
+        ax.add_patch(circle)
+        ax.text(
+            x, y, f"{percentages[i]:.1f}%", fontsize=12, fontweight="bold",
+            ha="center", va="center", color="white"
+        )
+
+    # Optionally, add a title (if desired)
+    plt.title(title, fontsize=14)
+
+    # Save the plot to a bytes buffer and return a PIL Image
+    img_buffer = io.BytesIO()
+    plt.savefig(img_buffer, format="png", bbox_inches="tight", facecolor=fig.get_facecolor())
+    img_buffer.seek(0)
+    plt.close(fig)
+    return Image.open(img_buffer
 def analyze_excel_single(file_path):
     """
     Analyzes a single Excel file containing data and generates plots for Trust, NPS, Loyalty, Consideration, and Satisfaction models.