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app.py

@@ -720,6 +720,7 @@ elif app_mode == 'Test Video Translation':
         output_file = f"/tmp/techmatrix_output_{uuid.uuid4().hex}.{format_name}"
         video_writer = None
         weighted_predictions = {}
+        frame_predictions = {}  # Reset for each video session
         
         frame_idx = 0
         

pose_utils.py

@@ -414,20 +414,34 @@ def create_bar_plot_visualization(image, predictions, title, orig_img):
         title: Plot title
         orig_img: Original image for sizing
     """
-    fig, ax = plt.subplots(figsize=(orig_img.shape[1]/100, orig_img.shape[0]/200), dpi=100)
-    plt.title(title)
-    
-    # Create bar plot data
-    labels = list(predictions.keys())
-    probabilities = list(predictions.values())
+    # Handle empty predictions case
+    if not predictions or len(predictions) == 0:
+        # Create a simple plot showing "No predictions available"
+        fig, ax = plt.subplots(figsize=(orig_img.shape[1]/100, orig_img.shape[0]/200), dpi=100)
+        ax.text(0.5, 0.5, 'No Predictions Available', 
+                horizontalalignment='center', verticalalignment='center',
+                transform=ax.transAxes, fontsize=14)
+        ax.set_title(title)
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        ax.set_xticks([])
+        ax.set_yticks([])
+    else:
+        fig, ax = plt.subplots(figsize=(orig_img.shape[1]/100, orig_img.shape[0]/200), dpi=100)
+        plt.title(title)
+        
+        # Create bar plot data
+        labels = list(predictions.keys())
+        probabilities = list(predictions.values())
 
-    # Create seaborn bar plot
-    sns.barplot(x=labels, y=probabilities, ax=ax)
-    plt.close(fig)  # Close to avoid memory leaks
+        # Create seaborn bar plot
+        sns.barplot(x=labels, y=probabilities, ax=ax)
+    
     fig.canvas.draw()
     
     # Convert plot to numpy array
     plot_image = np.array(fig.canvas.renderer.buffer_rgba())[:, :, :3]  # Remove alpha
+    plt.close(fig)  # Close to avoid memory leaks
 
     # Combine image and plot vertically
     combined_image = np.vstack((image, cv2.resize(plot_image, (image.shape[1], plot_image.shape[0]))))