# File: streamlit_dashboard.py
import streamlit as st
import requests
import pandas as pd
import plotly.express as px

st.set_page_config(
    page_title="Data Visualizer - Rajeev",
    page_icon="📊",  # Can also use emoji
)

BASE_URL = "https://splashdatavisualize-production.up.railway.app/api/visualizations"

st.title("📊 Sales Visualization Dashboard")
st.markdown("Use the sidebar to choose a visualization.")

chart_type = st.sidebar.selectbox("Select a Chart", [
    "Sales by Country",
    "Sales by Category",
    "Share by Country",
    "Share by Category",
    "Geo Sales",
    "Grouped Country & Category",
    "Stacked Country & Category"
])

# 1. Sales by Country
if chart_type == "Sales by Country":
    data = requests.get(f"{BASE_URL}/sales-by-country").json()
    df = pd.DataFrame(data.items(), columns=["Country", "Sales"])
    st.plotly_chart(px.bar(df, x="Country", y="Sales", title="Sales by Country"))

# 2. Sales by Category
elif chart_type == "Sales by Category":
    data = requests.get(f"{BASE_URL}/sales-by-category").json()
    df = pd.DataFrame(data.items(), columns=["Category", "Sales"])
    st.plotly_chart(px.bar(df, x="Category", y="Sales", title="Sales by Category"))

# 3. Share by Country (Pie Chart)
elif chart_type == "Share by Country":
    data = requests.get(f"{BASE_URL}/share-by-country").json()
    df = pd.DataFrame(data.items(), columns=["Country", "Share %"])
    st.plotly_chart(px.pie(df, names="Country", values="Share %", title="Share by Country"))

# 4. Share by Category (Pie Chart)
elif chart_type == "Share by Category":
    data = requests.get(f"{BASE_URL}/share-by-category").json()
    df = pd.DataFrame(data.items(), columns=["Category", "Share %"])
    st.plotly_chart(px.pie(df, names="Category", values="Share %", title="Share by Category"))

# 5. Geo Sales
elif chart_type == "Geo Sales":
    data = requests.get(f"{BASE_URL}/geo-sales").json()
    df = pd.DataFrame(data)
    st.plotly_chart(px.choropleth(df, locations="country", locationmode="country names",
                                   color="value", title="Geo Sales by Country", color_continuous_scale="Blues"))

# 6. Grouped Country & Category
elif chart_type == "Grouped Country & Category":
    data = requests.get(f"{BASE_URL}/grouped-country-category").json()
    df = pd.DataFrame(data).T.reset_index().rename(columns={"index": "Country"})
    df = df.melt(id_vars="Country", var_name="Category", value_name="Sales")
    st.plotly_chart(px.bar(df, x="Country", y="Sales", color="Category", barmode="group",
                           title="Grouped Sales by Country and Category"))

# 7. Stacked Country & Category
elif chart_type == "Stacked Country & Category":
    data = requests.get(f"{BASE_URL}/stacked-country-category").json()
    df = pd.DataFrame(data)
    df = df.melt(id_vars="country", var_name="Category", value_name="Sales")
    st.plotly_chart(px.bar(df, x="country", y="Sales", color="Category", barmode="stack",
                           title="Stacked Sales by Country and Category"))