Update app.py

app.py

@@ -74,9 +74,6 @@ 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.
@@ -89,117 +86,6 @@ def plot_model_results(results_df, average_value, title, model_type):
         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_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
     )
@@ -590,11 +476,7 @@ def plot_trust_driver_bubbles(trust_df, title, bubble_positions=None, gap=-0.2):
     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.
@@ -782,7 +664,6 @@ def analyze_excel_single(file_path):
                 "NPS",
             )
 
-
         # plot loyalty
         img_loyalty = None
         results_df_loyalty = None
@@ -1135,13 +1016,14 @@ def variable_outputs(file_inputs):
         # Get dataset name
         dataset_name = file_inputs_single[row].split("/")[-1]
 
+        # Based on the number of files uploaded, determine the content of each textbox
         plots = [
             gr.Markdown(
                 "<span style='font-size:20px; font-weight:bold;'>2) Trust Profile</span>",
                 visible=True,
             ),
             gr.Markdown(
-                "<div style='font-size:16px;'>This analysis shows you how strongly you are trusted in each of the Six Buckets of Trust®. You can also see this for any competitor.</div>",
+                "This analysis shows you how strongly you are trusted in each of the Six Buckets of Trust®. You can also see this for any competitor.",
                 visible=True,
             ),
             gr.Image(
@@ -1156,17 +1038,15 @@ def variable_outputs(file_inputs):
             ),
             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>
+                This analysis highlights which Trust Buckets® are most effective in building trust and positively impacting your key performance indicators (KPIs).
+                <br>
+                The Trust Buckets® that extend further to the right represent those with greater importance. We illustrate how each bucket over- or under-indexes against the average. The baseline impact for each driver is 16.7% (100% divided across 6 Trust Buckets®). Any percentage above this average indicates higher significance, meaning these Trust Buckets® require more attention. To maximize their potential, focus on “filling” them with the right attributes and tailored messaging.
                 """,
                 visible=True,
             ),
-        
-            
+         
+
+
             gr.Image(
                 value=img_trust,
                 type="pil",
@@ -1179,7 +1059,6 @@ def variable_outputs(file_inputs):
                 label="NPS Drivers",
                 visible=True,
             ),
-            
             gr.Image(
                 value=img_loyalty,
                 type="pil",
@@ -1201,6 +1080,8 @@ def variable_outputs(file_inputs):
             ),
         ]
 
+        # add current plots to container
+        plots_visible += plots
 
         if isinstance(df_builder_pivot, pd.DataFrame):
             logger.debug(f"df_builder_pivot: {df_builder_pivot}")
@@ -2258,25 +2139,31 @@ def highlight_button(button_name):
 with gr.Blocks(css=css,js=js_func) as demo:
     with gr.Column():
         gr.Markdown(
-                """
-                <h2 style="text-align: center; font-size: 2.25rem; font-weight: 600;">
-                    What drives your NPS and trust?
-                </h2>
-                """
-            )
-        # gr.HTML(
-        #     """
-        #     <div style="text-align: center;">
-        #         <img src="https://huggingface.co/spaces/trustlogic/temporary-trustlogic-demo/resolve/main/screenshot.png" 
-        #              alt="Trust Buckets" style="max-width: 100%; height: auto; border: none;">
-        #     </div>
-        #     """
-        # )
+        """     
+        ### Which Trust Buckets® are the most important to your audience and how do you fill them best?
+        
+        """
+    )
+        gr.HTML(
+            """
+            <div style="text-align: center;">
+                <img src="https://huggingface.co/spaces/trustlogic/temporary-trustlogic-demo/resolve/main/screenshot.png" 
+                     alt="Trust Buckets" style="max-width: 100%; height: auto; border: none;">
+            </div>
+            """
+        )
 
 
         gr.Markdown(
         """     
-        Quickly identify what drives your NPS and trust across different segments using the automated analyser.
+        To use TrustAnalyser®, you first insert the TrustLogic® quantitative statements into any of your surveys.
+        The module consists of six statements that have been extensively modeled, calibrated, and proven in
+        practice for over 14 years.
+        
+        Once your survey results are in, you can export the data as a CSV. Use our Excel template, or seamlessly
+        link your SurveyMonkey survey to our Analyser interface. From there, TrustAnalyser® helps you quickly
+        identify which Trust Buckets® resonate most with your audience and the specific TrustBuilders® that
+        will fill them effectively.
         """
     )
 
@@ -2286,6 +2173,7 @@ with gr.Blocks(css=css,js=js_func) as demo:
         <span style="font-size:15px;">Volkswagen Example</span><br>
         As a default, the analysis displays <strong>Volkswagen Owner</strong> results. To trigger the analysis for 
         <strong>Prospects</strong>, toggle to ‘VW Prospects’. 
+        If you include your competitors in your survey, you can also see their results.
        
         """,
         visible=True,