import streamlit as st 
import requests
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt

API_BASE_URL = "http://localhost:8000"
CSV_FILE_PATH = "src/data/merged_yt_data.csv"
KAGGLE_LINK = "https://www.kaggle.com/datasets/rsrishav/youtube-trending-video-dataset?select=IN_category_id.json"

st.set_page_config(
    page_title="YouTube Trending Insights",
    layout="wide",
    initial_sidebar_state="expanded"
)

st.markdown("""
    <style>
    .main {
        background-color: #f5f5f5;
        padding: 20px;
        border-radius: 10px;
    }
    .title {
        color: #ff0000;
        font-family: 'Arial', sans-serif;
        text-align: center;
        padding: 20px 0;
    }
    .subtitle {
        color: #333333;
        font-family: 'Arial', sans-serif;
        padding: 10px 0;
    }
    .stButton>button {
        background-color: #ff0000;
        color: white;
        border-radius: 5px;
        padding: 10px 20px;
    }
    .stButton>button:hover {
        background-color: #cc0000;
    }
    </style>
""", unsafe_allow_html=True)

# Sidebar with Logo and Select Box
with st.sidebar:
    st.markdown("""
        <div style='text-align:center;'>
            <img src='https://upload.wikimedia.org/wikipedia/commons/b/b8/YouTube_Logo_2017.svg' width='80%'>
        </div>
    """, unsafe_allow_html=True)
    
    st.markdown("<h2 class='subtitle'>📊 YouTube Analytics</h2>", unsafe_allow_html=True)
    

       # Kaggle Dataset Link
    st.sidebar.markdown("""
        <h3 style='text-align:center;'>📂 Kaggle Dataset</h3>
        <p style='text-align:center;'>
            <a href='{}' target='_blank' style='text-decoration:none;'>
                <button style='background-color:#ff0000; color:white; padding:10px 20px; border:none; border-radius:5px; cursor:pointer;'>
                    🔗 Open Kaggle Dataset
                </button>
            </a>
        </p>
    """.format(KAGGLE_LINK), unsafe_allow_html=True)
    # Analysis Options
    options = {
        "📈 Trending Videos Over Time": "/trending_videos_count",
        "🥧 Most Popular Categories": "/most_popular_categories",
        "📊 Like Ratio Distribution": "/engagement/like_ratio_distribution",
        "👍 Top Liked Videos": "/engagement/top_liked_videos",
        "🏆 Top Trending Channels": "/channel-performance/top-trending",
        "📅 Channel Growth Over Time": "/channel-performance/growth-over-time",
        "❤️ Category Like-View Ratio": "/category-like-view-ratio",
        "💬 Category Comment Engagement": "/category-comment-engagement"
    }

    selected_option = st.selectbox("Choose an analysis:", list(options.keys()), 
                                 help="Select a visualization to explore YouTube trends")

def fetch_data(endpoint):
    try:
        response = requests.get(f"{API_BASE_URL}{endpoint}")
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        st.error(f"Failed to fetch data: {e}")
        return None

st.markdown(f"<h2 class='subtitle'>{selected_option}</h2>", unsafe_allow_html=True)
data = fetch_data(options[selected_option])

if data:
    if "Trending Videos" in selected_option:
        df = pd.DataFrame(data["trending_video_counts"].items(), columns=["Date", "Count"])
        df["Date"] = pd.to_datetime(df["Date"])
        fig = px.line(df, x="Date", y="Count", title="Trending Videos Over Time")
        st.plotly_chart(fig, use_container_width=True)

    elif "Popular Categories" in selected_option:
        df = pd.DataFrame.from_dict(data["most_popular_categories"], orient='index', columns=["Count"])
        fig = px.pie(df, names=df.index, values="Count", title="Popular Categories",
                    hole=0.4, color_discrete_sequence=px.colors.sequential.RdBu)
        st.plotly_chart(fig, use_container_width=True)

    elif "Like Ratio" in selected_option:
        df = pd.DataFrame(data["like_ratio_distribution"])
        fig = px.histogram(df, x="like_ratio", nbins=50, title="Like Ratio Distribution",
                         color_discrete_sequence=['#ff0000'])
        st.plotly_chart(fig, use_container_width=True)

    elif "Top Liked Videos" in selected_option:
        df = pd.DataFrame(data["top_liked_videos"])
        fig = px.bar(df, x="title", y="likes", title="🔝 Top Liked Videos",
                    color="likes", color_continuous_scale="Reds")
        st.plotly_chart(fig, use_container_width=True)

       

    elif "Top Trending Channels" in selected_option:
        df = pd.DataFrame(data["top_trending_channels"].items(), columns=["Channel", "Trending Count"])
        df = df.sort_values(by="Trending Count", ascending=False).head(10)
        fig = px.bar(df, x="Channel", y="Trending Count", title="Top Trending Channels",
                    color="Trending Count", color_continuous_scale="Reds")
        st.plotly_chart(fig, use_container_width=True)

    elif "Channel Growth" in selected_option:
        df = pd.DataFrame(data)
        fig = px.line(df, x="published_month", y="video_count", color="channelTitle",
                     title="Channel Growth Over Time", line_shape="spline")
        st.plotly_chart(fig, use_container_width=True)

    elif "Like-View Ratio" in selected_option:
        df = pd.DataFrame(data["data"])
        fig = px.sunburst(df, path=["category_name"], values="like_view_ratio",
                        title="Category Like-View Ratio", color="like_view_ratio",
                        color_continuous_scale="RdYlBu")
        st.plotly_chart(fig, use_container_width=True)

    elif "Comment Engagement" in selected_option:
        df = pd.DataFrame(data["data"])
        fig = px.treemap(df, path=["category_name"], values="comment_count",
                       title="Category Comment Engagement", color="comment_count",
                       color_continuous_scale="Blues")
        st.plotly_chart(fig, use_container_width=True)

# Dataset Preview
st.sidebar.markdown("<h2 class='subtitle'>📋 Dataset Preview</h2>", unsafe_allow_html=True)
with st.sidebar.expander("View Raw Dataset", expanded=False):
    if st.button("Show Dataset Preview"):
        try:
            df_csv = pd.read_csv(CSV_FILE_PATH)
            st.dataframe(df_csv.head(1000), use_container_width=True)
        except Exception as e:
            st.error(f"Error loading dataset: {e}")