Update app.py

app.py

@@ -1,21 +1,173 @@
 import gradio as gr
-import plotly.express as px
-import numpy as np
-
-# Generate a simple plotly graph (e.g., a sine wave)
-def generate_graph():
-    x = np.linspace(0, 2 * np.pi, 100)
-    y = np.sin(x)
-    fig = px.line(x=x, y=y, title="Sine Wave")
+import pandas as pd
+import os
+
+# Function to calculate the follower increase from the CSV
+def calculate_follower_increase():
+    csv_path = "Followers.csv"  # Use the hardcoded path directly
+    df = pd.read_csv(csv_path)
+
+    # Ensure proper column naming and strip out any extra spaces
+    df.columns = df.columns.str.strip()
+    
+    # Sort by date to get the two most recent entries
+    df['Date'] = pd.to_datetime(df['Date'])
+    df = df.sort_values(by='Date', ascending=False)
+
+    # Get the two most recent entries
+    recent_data = df.head(2)
+    
+    # Calculate the differences for each platform
+    tiktok_increase = recent_data.iloc[0]['TikTok Followers'] - recent_data.iloc[1]['TikTok Followers']
+    facebook_increase = recent_data.iloc[0]['Facebook Followers'] - recent_data.iloc[1]['Facebook Followers']
+
+    # Calculate the total increase across platforms
+    total_increase = tiktok_increase + facebook_increase
+
+    return f"Leonardo got you {int(total_increase)} more followers across TikTok & Facebook!"
+
+# Function to calculate the engagement increase from the CSV
+def calculate_engagement_increase():
+    csv_path = "Total Engagement.csv"  # Path to the uploaded CSV
+    df = pd.read_csv(csv_path)
+
+    # Ensure proper column naming and strip out any extra spaces
+    df.columns = df.columns.str.strip()
+    
+    # Sort by date to get the two most recent entries
+    df['Date'] = pd.to_datetime(df['Date'])
+    df = df.sort_values(by='Date', ascending=False)
+
+    # Get the two most recent entries
+    recent_data = df.head(2)
+    
+    # Calculate the difference in total engagement
+    engagement_increase = recent_data.iloc[0]['Total Engagement Across Our Posts'] - recent_data.iloc[1]['Total Engagement Across Our Posts']
+
+    return f"Leonardo got you {int(engagement_increase)} more engagements across TikTok & Facebook!"
+
+# Function to extract the prediction label from filenames
+def extract_prediction_label_from_masterpiece(filename):
+    return filename.split('_')[-1].replace('.mp4', '')
+
+# 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
 
-# Path to a video file (you can replace this with your own video file)
-video_path = "set up teaser.mp4"
+# Function to load and plot the masterpiece data (video and graph)
+def load_masterpiece_video_and_plot():
+    masterpiece_video_path = "starperformer_viral.mp4"
+    
+    # Replace [actual outcome] with the correct label
+    actual_outcome = extract_prediction_label_from_masterpiece(masterpiece_video_path)
+    
+    # Load the data for the TikTok graph (simulated example)
+    tiktok_csv_path = "tiktok_histogram.csv"
+    df = pd.read_csv(tiktok_csv_path)
+
+    # Assuming the CSV has columns 'Engagement' and 'Density'
+    x_data = df['Engagement']
+    y_data = df['Density']
+
+    # Get x_max for the quintile calculation
+    x_max = max(x_data)
+
+    # Create the plot with actual data
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=x_data, y=y_data, mode='lines', name="Actual Data"))
+
+    # Highlight the quintile based on the actual outcome
+    fig = highlight_masterpiece_quintile(fig, actual_outcome, x_max)
+
+    # Return the video path and graph
+    return masterpiece_video_path, fig
 
-# Create a Gradio interface with both a graph and a video displayed side by side
-with gr.Blocks() as demo:
+# Create the Gradio interface
+with gr.Blocks() as dashboard:
+
+    # Section for Leonardo's Last-Week Masterpiece
+    gr.Markdown("## Leonardo's Last-Week Masterpiece")
+    with gr.Row():
+        masterpiece_video, masterpiece_graph = load_masterpiece_video_and_plot()
+        gr.Video(masterpiece_video, label="Star Performer TikTok Video")
+        gr.Plot(masterpiece_graph, label="Star Performer TikTok Performance")
+
+    # Add a header for follower impact
+    gr.Markdown("## Leonardo's Follower Impact")
+
+    # Create the textbox for follower count (initial message)
+    follower_text = gr.Textbox(label="Follower Update", value="Click the button to see the increase.", interactive=False)
+
+    # Add the button and set it to update the follower count
+    with gr.Row():
+        gr.Button("See New Followers").click(fn=calculate_follower_increase, inputs=[], outputs=[follower_text])
+
+    # Add a header for engagement impact
+    gr.Markdown("## Leonardo's Engagement Impact")
+
+    # Create the textbox for engagement count (initial message)
+    engagement_text = gr.Textbox(label="Engagement Update", value="Click the button to see the increase.", interactive=False)
+
+    # Add the button and set it to update the engagement count
     with gr.Row():
-        graph = gr.Plot(generate_graph(), label="Graph")
-        video = gr.Video(video_path, label="Sample Video")
+        gr.Button("See New Engagement").click(fn=calculate_engagement_increase, inputs=[], outputs=[engagement_text])
+
+    # Add the main title
+    gr.Markdown("# Leonardo's Plans for This Week")
+
+    # Section for TikTok projections (Videos)
+    with gr.Row():
+        with gr.Column():
+            video_path_1, graph_1 = load_data_and_plot("video", "TikTok1", tiktok_csv_path, video_folder)
+            if video_path_1:
+                gr.Video(video_path_1, label="TikTok 1 Video")
+                gr.Plot(graph_1, label="TikTok 1 Performance")
+
+        with gr.Column():
+            video_path_2, graph_2 = load_data_and_plot("video", "TikTok2", tiktok_csv_path, video_folder)
+            if video_path_2:
+                gr.Video(video_path_2, label="TikTok 2 Video")
+                gr.Plot(graph_2, label="TikTok 2 Performance")
+
+        with gr.Column():
+            video_path_3, graph_3 = load_data_and_plot("video", "TikTok3", tiktok_csv_path, video_folder)
+            if video_path_3:
+                gr.Video(video_path_3, label="TikTok 3 Video")
+                gr.Plot(graph_3, label="TikTok 3 Performance")
+
+    # Section for Facebook projections (Statics)
+    with gr.Row():
+        with gr.Column():
+            image_path_1, graph_4 = load_data_and_plot("image", "Static1", facebook_csv_path, image_folder)
+            if image_path_1:
+                gr.Image(image_path_1, label="Facebook Static 1")
+                gr.Plot(graph_4, label="Facebook Static 1 Performance")
+
+        with gr.Column():
+            image_path_2, graph_5 = load_data_and_plot("image", "Static2", facebook_csv_path, image_folder)
+            if image_path_2:
+                gr.Image(image_path_2, label="Facebook Static 2")
+                gr.Plot(graph_5, label="Facebook Static 2 Performance")
+
+        with gr.Column():
+            image_path_3, graph_6 = load_data_and_plot("image", "Static3", facebook_csv_path, image_folder)
+            if image_path_3:
+                gr.Image(image_path_3, label="Facebook Static 3")
+                gr.Plot(graph_6, label="Facebook Static 3 Performance")
 
-demo.launch()
+# Launch the dashboard
+dashboard.launch(debug=True)