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app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+
+# Load datasets
+df_category = pd.read_csv("cleaned_vahan.csv", parse_dates=["Date"])
+df_manufacturer = pd.read_csv("cleaned_vahan_final.csv", parse_dates=["Date"])
+
+# Growth calculations function
+def add_growth(df):
+    df = df.sort_values(["Category", "Date"])
+    df["YoY_Growth"] = df.groupby("Category")["Registrations"].pct_change(periods=12) * 100
+    df["QoQ_Growth"] = df.groupby("Category")["Registrations"].pct_change(periods=3) * 100
+    return df
+
+df_category = add_growth(df_category)
+df_manufacturer = add_growth(df_manufacturer)
+
+st.title("Vahan Vehicle Registrations Dashboard")
+
+st.sidebar.header("Filters")
+
+# ✅ Radio button in sidebar
+view_option = st.sidebar.radio("View Mode", ["Vehicle Type", "Manufacturer"])
+
+# Date range filter (common)
+if view_option == "Vehicle Type":
+    date_range = st.sidebar.date_input(
+        "Select Date Range",
+        [df_category["Date"].min(), df_category["Date"].max()]
+    )
+else:
+    date_range = st.sidebar.date_input(
+        "Select Date Range",
+        [df_manufacturer["Date"].min(), df_manufacturer["Date"].max()]
+    )
+
+start_date = pd.to_datetime(date_range[0])
+end_date = pd.to_datetime(date_range[1])
+
+filtered_df = pd.DataFrame()  # start empty
+
+# -------------------------------
+# Vehicle Type Mode
+# -------------------------------
+if view_option == "Vehicle Type":
+    categories = st.sidebar.multiselect(
+        "Vehicle Types (type to search)",
+        options=sorted(df_category["Category"].dropna().astype(str).str.strip().unique()),
+        default=[]  # empty until user picks
+    )
+    if categories:
+        mask = (df_category["Category"].isin(categories)) & (df_category["Date"].between(start_date, end_date))
+        filtered_df = df_category[mask]
+
+# -------------------------------
+# Manufacturer Mode
+# -------------------------------
+else:
+    manufacturers = sorted(df_manufacturer["Category"].dropna().astype(str).str.strip().unique())
+
+    selected_manu = st.sidebar.multiselect(
+        "Manufacturers (type to search, pick up to 5)",
+        options=manufacturers,
+        default=[],  # empty until user picks
+        # If your Streamlit version supports it, uncomment the next line:
+        # max_selections=5
+    )
+
+    # Fallback cap if your Streamlit version doesn’t support max_selections
+    if len(selected_manu) > 5:
+        st.sidebar.warning("Select up to 5 manufacturers. Using the first 5 you picked.")
+        selected_manu = selected_manu[:5]
+
+    if selected_manu:
+        mask = (df_manufacturer["Category"].isin(selected_manu)) & (df_manufacturer["Date"].between(start_date, end_date))
+        filtered_df = df_manufacturer[mask]
+
+# -------------------------------
+# Growth Toggle
+# -------------------------------
+growth_option = st.radio(
+    "Select Growth Metric",
+    ("None", "Year-over-Year Growth (%)", "Quarter-over-Quarter Growth (%)"),
+    horizontal=True
+)
+
+# -------------------------------
+# Chart
+# -------------------------------
+if not filtered_df.empty:
+    if growth_option == "Year-over-Year Growth (%)":
+        fig = px.line(filtered_df, x="Date", y="YoY_Growth", color="Category",
+                      title="Year-over-Year Growth (%)")
+    elif growth_option == "Quarter-over-Quarter Growth (%)":
+        fig = px.line(filtered_df, x="Date", y="QoQ_Growth", color="Category",
+                      title="Quarter-over-Quarter Growth (%)")
+    else:
+        fig = px.line(filtered_df, x="Date", y="Registrations", color="Category",
+                      title="Monthly Vehicle Registrations")
+    st.plotly_chart(fig, use_container_width=True)
+
+    st.markdown("---")  # separator
+
+    st.subheader("📌 Key Insights")
+
+    # Total registrations
+    total_reg = filtered_df["Registrations"].sum()
+
+    # Group by Category/Manufacturer for rankings
+    grouped = filtered_df.groupby("Category")["Registrations"].sum().reset_index()
+
+    # Best performer
+    best = grouped.loc[grouped["Registrations"].idxmax()]
+    best_cat, best_val = best["Category"], best["Registrations"]
+
+    # Worst performer
+    worst = grouped.loc[grouped["Registrations"].idxmin()]
+    worst_cat, worst_val = worst["Category"], worst["Registrations"]
+
+    # Avg YoY growth (if column exists)
+    avg_yoy = filtered_df["YoY_Growth"].mean(skipna=True)
+
+    # Show metrics in 4 columns
+    col1, col2, col3, col4 = st.columns(4)
+    col1.metric("Total Registrations", f"{total_reg:,}")
+    col2.metric("Top Performer", best_cat, f"{best_val:,}")
+    col3.metric("Lowest Performer", worst_cat, f"{worst_val:,}")
+    col4.metric("Avg YoY Growth", f"{avg_yoy:.2f}%")
+
+    
+else:
+    st.info("👆 Choose at least one Vehicle Type or Manufacturer to see results.")

cleaned_vahan.csv

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+Category,Year,Month,Month_Num,Date,Registrations
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+2-WHEELER,2022,AUG,8,2022-08-01,1181462
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+2-WHEELER,2022,JAN,1,2022-01-01,1154846
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