Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,72 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import re
 import streamlit as st
+import pandas as pd
+import joblib
+
+# Load trained model
+MODEL_PATH = "superkart_sales_prediction_model_v1_0.joblib"
+model = joblib.load(MODEL_PATH)
+
+valid_store_ids = ["OUT001", "OUT002", "OUT003", "OUT004"]
+store_meta = {
+    "OUT001": {"year": 1987, "size": "High",   "city_type": "Tier 2", "store_type": "Supermarket Type1"},
+    "OUT002": {"year": 1998, "size": "Small",  "city_type": "Tier 3", "store_type": "Food Mart"},
+    "OUT003": {"year": 1999, "size": "Medium", "city_type": "Tier 1", "store_type": "Departmental Store"},
+    "OUT004": {"year": 2009, "size": "Medium", "city_type": "Tier 2", "store_type": "Supermarket Type2"},
+}
+
+st.title("SmartKart: Product Sales Prediction")
+st.subheader("Online Prediction")
+
+# Product ID validation
+product_id = st.text_input("Product ID (2 uppercase letters + 4 digits, e.g., FD6114)", max_chars=6)
+product_id_valid = bool(re.fullmatch(r"[A-Z]{2}\d{4}", product_id))
+if product_id and not product_id_valid:
+    st.error("Invalid Product ID format; it must be 2 uppercase letters followed by 4 digits.")
+elif product_id_valid:
+    st.success("Product ID format is valid.")
+
+# Store selection
+store_id = st.selectbox("Store ID", options=valid_store_ids)
+meta = store_meta[store_id]
+st.text(f"Store Establishment Year: {meta['year']}")
+st.text(f"Store Size: {meta['size']}")
+st.text(f"Store Location City Type: {meta['city_type']}")
+st.text(f"Store Type: {meta['store_type']}")
+
+# Product features
+product_weight = st.number_input("Product Weight", min_value=0.0, value=12.0, format="%.2f")
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_allocated_area = st.slider("Product Allocated Area Ratio", min_value=0.0, max_value=1.0, step=0.001, value=0.05)
+product_type = st.selectbox("Product Type", [
+    "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks",
+    "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
+    "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods", "Others"
+])
+product_mrp = st.number_input("Product MRP", min_value=0.0, value=150.0, format="%.2f")
+
+# Prepare dataframe
+input_data = pd.DataFrame([{
+    "Product_Id": product_id,
+    "Product_Weight": product_weight,
+    "Product_Sugar_Content": product_sugar_content,
+    "Product_Allocated_Area": product_allocated_area,
+    "Product_Type": product_type,
+    "Product_MRP": product_mrp,
+    "Store_Id": store_id,
+    "Store_Establishment_Year": meta['year'],
+    "Store_Size": meta['size'],
+    "Store_Location_City_Type": meta['city_type'],
+    "Store_Type": meta['store_type'],
+}])
 
-"""
-# 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])),
-    ))
+# Prediction
+if st.button("Predict"):
+    if not product_id_valid:
+        st.error("Please fix the Product ID before proceeding.")
+    else:
+        try:
+            prediction = model.predict(input_data)[0]
+            st.success(f"Predicted Product Sales: {prediction:.2f}")
+        except Exception as e:
+            st.error(f"Prediction failed: {e}")