Update app.py

app.py

@@ -60,13 +60,14 @@ def calculate_engagement_increase():
 
 # Function to load and plot the masterpiece data (video and graph)
 def load_masterpiece_video_and_plot():
-    masterpiece_video_path = download_file_from_repo("starperformer_viral.mp4")  # Download video from Hugging Face
-    
-    # Replace [actual outcome] with the correct label
-    actual_outcome = extract_prediction_label_from_masterpiece(masterpiece_video_path)
+    # Download the video file from the Hugging Face repository
+    masterpiece_video_path = hf_hub_download(repo_id="conhllnd/Playper-V2", filename="starperformer_viral.mp4", repo_type="space")
+
+    # Replace [actual outcome] with the correct label (here it's 'viral' from the filename)
+    actual_outcome = "viral"
     
     # Load the data for the TikTok graph (simulated example)
-    tiktok_csv_path = download_file_from_repo("tiktok_histogram.csv")  # Download CSV from Hugging Face
+    tiktok_csv_path = hf_hub_download(repo_id="conhllnd/Playper-V2", filename="tiktok_histogram.csv", repo_type="space")
     df = pd.read_csv(tiktok_csv_path)
 
     # Assuming the CSV has columns 'Engagement' and 'Density'
@@ -86,6 +87,24 @@ def load_masterpiece_video_and_plot():
     # Return the video path and graph
     return masterpiece_video_path, fig
 
+# Function to highlight the quintile based on prediction label for the masterpiece
+def highlight_masterpiece_quintile(fig, actual_outcome, x_max):
+    label_map = {"normal": 1, "good": 2, "very good": 3, "exceptional": 4, "viral": 5}
+    quintile = label_map.get(actual_outcome.lower(), 1)
+
+    # Dynamically adjust the x0 and x1 ranges based on x_max
+    quintile_width = x_max / 5  # Divide x_max into 5 quintiles
+    x0 = (quintile - 1) * quintile_width
+    x1 = quintile * quintile_width
+
+    # Add a shaded region in the graph corresponding to the quintile
+    fig.add_vrect(
+        x0=x0, x1=x1,
+        fillcolor="green", opacity=0.25, line_width=0,
+        annotation_text=f"Actual Outcome: {actual_outcome.capitalize()}"
+    )
+    return fig
+
 # Create the Gradio interface
 with gr.Blocks() as dashboard: