import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import Streamlit_functions as sf
import response_curves_model_quality_base as rc1
st.set_page_config(
    layout="wide"
)   
from pptx import Presentation
from pptx.util import Inches
from io import BytesIO
import plotly.io as pio
import Streamlit_functions as sf
import response_curves_model_quality_base as rc1


def save_ppt_file():

    # Initialize PowerPoint presentation
    prs = Presentation()
    # Helper function to add Plotly figure to slide
    def add_plotly_chart_to_slide(slide, fig, left, top, width, height):
        img_stream = BytesIO()
        pio.write_image(fig, img_stream, format='png',engine="orca")
        slide.shapes.add_picture(img_stream, left, top, width, height)

    # Slide 1: Model Quality with Chart
    slide_1 = prs.slides.add_slide(prs.slide_layouts[5])
    title_1 = slide_1.shapes.title
    title_1.text = "Model Quality"
    i = 0 
    # print (i)
    # Generate Plotly chart
    fig = sf.mmm_model_quality()

    # Add the Plotly chart to the slide
    add_plotly_chart_to_slide(slide_1, fig, Inches(1), Inches(2), width=Inches(9), height=Inches(4.5))
    i = i+1
    # print (i)
    # Slide 2: Media Data Elasticity
    slide_2 = prs.slides.add_slide(prs.slide_layouts[5])
    title_2 = slide_2.shapes.title
    title_2.text = "Media Data Elasticity"
    i = i+1
    # print (i)
    # Generate Elasticity chart
    media_df = sf.media_data()
    fig = sf.elasticity(media_df)
    fig.update_layout(
        margin=dict(l=150, r=50, t=50, b=50),  # Adjust margins
        # xaxis=dict(tickangle=-45)  # Rotate x-axis labels if needed
    )
    i = i+1
    # print (i)
    # Add the Plotly chart to the slide
    add_plotly_chart_to_slide(slide_2, fig, Inches(1), Inches(2), width=Inches(8), height=Inches(4.5))
    i = i+1
    # print (i)
    # Slide 3: Half-Life Analysis
    slide_3 = prs.slides.add_slide(prs.slide_layouts[5])
    title_3 = slide_3.shapes.title
    title_3.text = "Half-Life Analysis"
    i = i+1
    # print (i)
    # Generate Half-Life chart
    fig = sf.half_life(media_df)
    fig.update_layout(
        margin=dict(l=150, r=100, t=50, b=50),  # Adjust margins
        # xaxis=dict(tickangle=-45)  # Rotate x-axis labels if needed
    )
    i = i+1
    # print (i)
    # Add the Plotly chart to the slide
    add_plotly_chart_to_slide(slide_3, fig, Inches(1), Inches(2), width=Inches(8), height=Inches(4.5))
    i = i+1
    # print (i)
    # Slide 4: Response Curves

    # Generate Response Curves chart
    channels = [
        'Broadcast TV',
        'Cable TV',
        'Connected & OTT TV',
        'Display Prospecting',
        'Display Retargeting',
        'Video',
        'Social Prospecting',
        'Social Retargeting',
        'Search Brand',
        'Search Non-brand',
        'Digital Partners',
        'Audio',
        'Email']
    i = 4
    for channel_name in channels:
        slide_4 = prs.slides.add_slide(prs.slide_layouts[5])
        title_4 = slide_4.shapes.title
        title_4.text = "Response Curves"
        i = i+1
        # print (i)
        selected_option = channel_name 
        selected_option2 = 'View Line Plot'
        fig = rc1.response_curves(selected_option, selected_option2)
        # Add the Plotly chart to the slide
        add_plotly_chart_to_slide(slide_4, fig, Inches(1), Inches(2), width=Inches(6), height=Inches(4.5))
    # Save the PowerPoint presentation
    # prs.save('MMM_Model_Quality_Presentation.pptx')
    # # print("PowerPoint slides created successfully.")

    # Save to a BytesIO object
    ppt_stream = BytesIO()
    prs.save(ppt_stream)
    ppt_stream.seek(0)
    
    return ppt_stream.getvalue()




st.header("Model Quality")
# st.write("MMM Model Quality")

st.plotly_chart(sf.mmm_model_quality(),use_container_width=True)
fig = sf.mmm_model_quality()
# print("aaa")
fig.write_image("chart.png",engine="orca")
# print("bbb")

media_df = sf.media_data()
    # Create two columns for start date and end date input
col1, col2 , col3 = st.columns([1,0.2,1])
df1 = sf.model_metrics_table_func()
st.dataframe(df1,hide_index  = True,use_container_width=True) 

# st.plotly_chart(sf.elasticity_and_media(media_df))
with col1:
    st.plotly_chart(sf.elasticity(media_df))
    fig = sf.elasticity(media_df)
    fig.write_image("chart.png",engine="orca")
with col2:
    st.write("")
with col3:
    st.plotly_chart(sf.half_life(media_df))
    fig = sf.elasticity(media_df)
    fig.write_image("chart.png",engine="orca")
    

# Dropdown menu options
options = [
    'Broadcast TV',
    'Cable TV',
    'Connected & OTT TV',
    'Display Prospecting',
    'Display Retargeting',
    'Video',
    'Social Prospecting',
    'Social Retargeting',
    'Search Brand',
    'Search Non-brand',
    'Digital Partners',
    'Audio',
    'Email']
options1 = [
    'View Line Plot',
    'View Scattered Plot',
    "View Both"]
col1, col2 = st.columns(2)
     # Create a dropdown menu   
with col1:
    selected_option = st.selectbox('Select A Media Channel:', options)
    selected_option2 = st.selectbox('Select A Chart Type', options1)
         # Display the selected option
    
with col2:
    st.write("")
st.plotly_chart(rc1.response_curves(selected_option,selected_option2))  

if st.button("Prepare Analysis Download"):
    ppt_file =  save_ppt_file()
        # Add a download button
    st.download_button(
            label="Download Analysis",
            data=ppt_file,
            file_name="MMM_Model_Quality_Presentation.pptx",
            mime="application/vnd.openxmlformats-officedocument.presentationml.presentation"
        )