Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,60 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# 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])),
-    ))
+import requests
+import json
+
+st.set_page_config(page_title="SuperKart Sales Prediction", layout="centered")
+
+st.title("🛒 SuperKart Sales Prediction")
+st.write("Enter product and store features below to get a sales forecast.")
+
+# --- INPUT FIELDS ---
+product_weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.1, value=12.0)
+product_sugar = st.selectbox("Product Sugar Content", [0, 1])
+product_alloc_area = st.number_input("Allocated Display Area (sq. m)", min_value=0.0, step=0.01, value=0.05)
+product_mrp = st.number_input("Product MRP", min_value=1.0, step=0.5, value=150.0)
+store_size = st.selectbox("Store Size", [0, 1, 2])  # small/medium/high
+store_city_type = st.selectbox("Store Location City Type", [0, 1, 2])  # a/b/c
+store_type = st.selectbox("Store Type", [0, 1, 2, 3])
+store_age = st.slider("Store Age (Years)", 0, 30, 10)
+
+product_type = st.selectbox("Product Category", [
+    "Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods", "Fruits and Vegetables",
+    "Hard Drinks", "Health and Hygiene", "Household", "Meat", "Others", "Seafood",
+    "Snack Foods", "Soft Drinks", "Starchy Foods"
+])
+
+# Create one-hot encoded product types
+product_type_features = {f"Product_Type_{pt}": int(pt == product_type) for pt in [
+    "Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods", "Fruits and Vegetables",
+    "Hard Drinks", "Health and Hygiene", "Household", "Meat", "Others", "Seafood",
+    "Snack Foods", "Soft Drinks", "Starchy Foods"
+]}
+
+# --- PAYLOAD CREATION ---
+input_data = {
+    "Product_Weight": product_weight,
+    "Product_Sugar_Content": product_sugar,
+    "Product_Allocated_Area": product_alloc_area,
+    "Product_MRP": product_mrp,
+    "Store_Size": store_size,
+    "Store_Location_City_Type": store_city_type,
+    "Store_Type": store_type,
+    "Store_Age": store_age,
+    **product_type_features
+}
+
+# --- API CALL ---
+if st.button("Predict Sales"):
+    with st.spinner("Fetching prediction from backend..."):
+        try:
+            response = requests.post(
+                "https://lokiiparihar-superkartbackendmodaldeploy-xgboost.hf.space/predict",
+                json=input_data
+            )
+            if response.status_code == 200:
+                prediction = response.json().get("prediction", "N/A")
+                st.success(f"🧮 Estimated Sales: **{prediction:.2f} units**")
+            else:
+                st.error(f"❌ API Error: {response.status_code}")
+        except Exception as e:
+            st.error(f"Request failed: {e}")