Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,67 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# File: streamlit_dashboard.py
 import streamlit as st
+import requests
+import pandas as pd
+import plotly.express as px
+
+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"))
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# 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"))