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import gradio as gr
import pandas as pd
import gradio as gr
import plotly.express as px
import pandas as pd
import numpy as np
from scipy.optimize import curve_fit
import plotly.graph_objects as go
import os
import datetime
import logging

# Example paths for engagement data
tiktok_csv_path = "tiktok_histogram.csv"
facebook_csv_path = "facebook_histogram.csv"
video_folder = "videos"
image_folder = "images"

# Function to load and plot the masterpiece data (video and graph)
def load_masterpiece_video_and_plot():
    masterpiece_video_path = os.path.join("starperformer_normal.mp4")

    # Assuming 'viral' is the actual outcome from the video filename
    actual_outcome = "normal"

    # Load the TikTok engagement data from the CSV
    fig, x_min, x_max, y_min, y_max = load_and_plot_csv(tiktok_csv_path)

    # Highlight the quintile based on the actual outcome 'viral'
    fig = highlight_quintile(fig, actual_outcome, x_max)

    return masterpiece_video_path, fig


# Function to extract the prediction label from filenames
def extract_prediction_label(filename, media_type):
    if media_type == "video":
        return filename.split('_')[3]  # Extracting the label from video filenames
    elif media_type == "image":
        return filename.split('_')[3]  # Extracting the label from image filenames

# Function to extract the version number from filenames
def extract_version_number(filename, media_type):
    if media_type == "video":
        version_with_v = filename.split('_')[1]  # For videos, version is at index 1
    elif media_type == "image":
        version_with_v = filename.split('_')[2]  # For images, version is at index 2
    
    version_number = version_with_v[1:]  # Remove the 'v' and keep only the number
    return version_number

# Function to get the highest prediction for each TikTok/Static type (e.g., TikTok1, Static2)
def get_highest_prediction_for_media(folder_path, media_type, media_identifier):
    highest_file = None
    highest_score = None

    # Loop over all files in the folder
    for filename in os.listdir(folder_path):
        if (media_type == "video" and filename.endswith('.mp4')) or (media_type == "image" and filename.endswith('.jpeg')):
            if media_identifier in filename:  # Check for TikTok1, TikTok2, etc.
                # Extract the prediction label from the file
                prediction_label = extract_prediction_label(filename, media_type)

                label_map = {"normal": 1, "good": 2, "very good": 3, "exceptional": 4, "viral": 5}
                score = label_map.get(prediction_label.lower(), 1)  # Default to 1 if not found

                # Check for highest score
                if highest_score is None or score > highest_score:
                    highest_score = score
                    highest_file = filename

    return highest_file

# Function to load and plot CSV data directly
def load_and_plot_csv(csv_path):
    df = pd.read_csv(csv_path)

    # Assuming the CSV has columns 'Engagement' and 'Density'
    x_data = df['Engagement']
    y_data = df['Density']

    # Get x_max and y_max for graph range
    x_max = max(x_data)
    y_max = max(y_data)

    # Create the plot with just the actual data
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=x_data, y=y_data, mode='lines', name="Actual Data"))

    return fig, x_data.min(), x_max, y_data.min(), y_max

# Function to highlight the quintile based on prediction label
def highlight_quintile(fig, label, x_max):
    label_map = {"normal": 1, "good": 2, "very good": 3, "exceptional": 4, "viral": 5}
    quintile = label_map.get(label.lower(), 1)  # Default to normal if label not found

    # 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"Predicted: {label.capitalize()}"
    )
    return fig

# Function to load video/image and its corresponding engagement data and plot the graph
def load_data_and_plot(media_type, media_identifier, csv_path, folder_path):
    # Get the file with the highest prediction for the given TikTok/Static type
    media_file = get_highest_prediction_for_media(folder_path, media_type, media_identifier)

    # Proceed if we found a valid file
    if media_file:
        prediction_label = extract_prediction_label(media_file, media_type)
        version_number = extract_version_number(media_file, media_type)

        # Get the full path to the media file
        media_path = os.path.join(folder_path, media_file)

        # Load and plot the actual data from the CSV
        fig, x_min, x_max, y_min, y_max = load_and_plot_csv(csv_path)

        # Highlight the quintile based on the label
        fig = highlight_quintile(fig, prediction_label, x_max)

        # Ensure the x-axis and y-axis start from 0 and dynamically set max values
        fig.update_layout(
            title=f"Predicted Engagement for {media_identifier} (Version {version_number})",  # Show version in title as "Version #"
            xaxis=dict(range=[0, x_max]),  # Start from 0 and go up to the max x value
            yaxis=dict(range=[0, y_max])   # Start from 0 and go up to the max y value
        )

        return media_path, fig
    return None, None



# 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 than last week on Instagram!"

# Create the Gradio interface
with gr.Blocks() as dashboard:
    # Section for Leonardo's Last-Week Masterpiece
    gr.Markdown("# Leonardo's Masterpiece from Last Week")
    
    # Load the masterpiece video and graph
    masterpiece_video, masterpiece_graph = load_masterpiece_video_and_plot()
    
    # Display the video
    gr.Video(masterpiece_video, label="Star Performer TikTok Video")
    
    # Display the graph with quintile highlighting
    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():
        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")

# Launch the dashboard
dashboard.launch(share=True, debug=True)

	import gradio as gr
	import pandas as pd
	import gradio as gr
	import plotly.express as px
	import pandas as pd
	import numpy as np
	from scipy.optimize import curve_fit
	import plotly.graph_objects as go
	import os
	import datetime
	import logging

	# Example paths for engagement data
	tiktok_csv_path = "tiktok_histogram.csv"
	facebook_csv_path = "facebook_histogram.csv"
	video_folder = "videos"
	image_folder = "images"

	# Function to load and plot the masterpiece data (video and graph)
	def load_masterpiece_video_and_plot():
	masterpiece_video_path = os.path.join("starperformer_normal.mp4")

	# Assuming 'viral' is the actual outcome from the video filename
	actual_outcome = "normal"

	# Load the TikTok engagement data from the CSV
	fig, x_min, x_max, y_min, y_max = load_and_plot_csv(tiktok_csv_path)

	# Highlight the quintile based on the actual outcome 'viral'
	fig = highlight_quintile(fig, actual_outcome, x_max)

	return masterpiece_video_path, fig


	# Function to extract the prediction label from filenames
	def extract_prediction_label(filename, media_type):
	if media_type == "video":
	return filename.split('_')[3] # Extracting the label from video filenames
	elif media_type == "image":
	return filename.split('_')[3] # Extracting the label from image filenames

	# Function to extract the version number from filenames
	def extract_version_number(filename, media_type):
	if media_type == "video":
	version_with_v = filename.split('_')[1] # For videos, version is at index 1
	elif media_type == "image":
	version_with_v = filename.split('_')[2] # For images, version is at index 2

	version_number = version_with_v[1:] # Remove the 'v' and keep only the number
	return version_number

	# Function to get the highest prediction for each TikTok/Static type (e.g., TikTok1, Static2)
	def get_highest_prediction_for_media(folder_path, media_type, media_identifier):
	highest_file = None
	highest_score = None

	# Loop over all files in the folder
	for filename in os.listdir(folder_path):
	if (media_type == "video" and filename.endswith('.mp4')) or (media_type == "image" and filename.endswith('.jpeg')):
	if media_identifier in filename: # Check for TikTok1, TikTok2, etc.
	# Extract the prediction label from the file
	prediction_label = extract_prediction_label(filename, media_type)

	label_map = {"normal": 1, "good": 2, "very good": 3, "exceptional": 4, "viral": 5}
	score = label_map.get(prediction_label.lower(), 1) # Default to 1 if not found

	# Check for highest score
	if highest_score is None or score > highest_score:
	highest_score = score
	highest_file = filename

	return highest_file

	# Function to load and plot CSV data directly
	def load_and_plot_csv(csv_path):
	df = pd.read_csv(csv_path)

	# Assuming the CSV has columns 'Engagement' and 'Density'
	x_data = df['Engagement']
	y_data = df['Density']

	# Get x_max and y_max for graph range
	x_max = max(x_data)
	y_max = max(y_data)

	# Create the plot with just the actual data
	fig = go.Figure()
	fig.add_trace(go.Scatter(x=x_data, y=y_data, mode='lines', name="Actual Data"))

	return fig, x_data.min(), x_max, y_data.min(), y_max

	# Function to highlight the quintile based on prediction label
	def highlight_quintile(fig, label, x_max):
	label_map = {"normal": 1, "good": 2, "very good": 3, "exceptional": 4, "viral": 5}
	quintile = label_map.get(label.lower(), 1) # Default to normal if label not found

	# 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"Predicted: {label.capitalize()}"
	)
	return fig

	# Function to load video/image and its corresponding engagement data and plot the graph
	def load_data_and_plot(media_type, media_identifier, csv_path, folder_path):
	# Get the file with the highest prediction for the given TikTok/Static type
	media_file = get_highest_prediction_for_media(folder_path, media_type, media_identifier)

	# Proceed if we found a valid file
	if media_file:
	prediction_label = extract_prediction_label(media_file, media_type)
	version_number = extract_version_number(media_file, media_type)

	# Get the full path to the media file
	media_path = os.path.join(folder_path, media_file)

	# Load and plot the actual data from the CSV
	fig, x_min, x_max, y_min, y_max = load_and_plot_csv(csv_path)

	# Highlight the quintile based on the label
	fig = highlight_quintile(fig, prediction_label, x_max)

	# Ensure the x-axis and y-axis start from 0 and dynamically set max values
	fig.update_layout(
	title=f"Predicted Engagement for {media_identifier} (Version {version_number})", # Show version in title as "Version #"
	xaxis=dict(range=[0, x_max]), # Start from 0 and go up to the max x value
	yaxis=dict(range=[0, y_max]) # Start from 0 and go up to the max y value
	)

	return media_path, fig
	return None, None



	# 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 than last week on Instagram!"

	# Create the Gradio interface
	with gr.Blocks() as dashboard:
	# Section for Leonardo's Last-Week Masterpiece
	gr.Markdown("# Leonardo's Masterpiece from Last Week")

	# Load the masterpiece video and graph
	masterpiece_video, masterpiece_graph = load_masterpiece_video_and_plot()

	# Display the video
	gr.Video(masterpiece_video, label="Star Performer TikTok Video")

	# Display the graph with quintile highlighting
	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():
	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")

	# Launch the dashboard
	dashboard.launch(share=True, debug=True)