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src/requirements.txt

@@ -0,0 +1,11 @@
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]
+streamlit==1.43.2

src/streamlit_app.py

@@ -1,40 +1,36 @@
-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 streamlit as st
+import joblib
+import pandas as pd
+
+st.title("SuperKart Store Sales Forecasting")
+
+# Input form
+st.subheader("Enter Product and Store Details")
+
+product_weight = st.number_input("Product Weight", value=10.0)
+sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+allocated_area = st.slider("Product Allocated Area", 0.01, 1.0, 0.1)
+product_type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household", "Baking Goods", "Canned", "Health and Hygiene", "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Starchy Foods", "Breakfast", "Seafood", "Others"])
+product_mrp = st.number_input("Product MRP", value=100.0)
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_city = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Supermarket Type2", "Supermarket Type1", "Departmental Store", "Food Mart"])
+store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
+store_age = st.slider("Store Age (Years)", 0, 50, 10)
+
+# Predict
+if st.button("Predict Sales"):
+    input_data = pd.DataFrame([{
+        'Product_Weight': product_weight,
+        'Product_Sugar_Content': sugar_content,
+        'Product_Allocated_Area': allocated_area,
+        'Product_Type': product_type,
+        'Product_MRP': product_mrp,
+        'Store_Size': store_size,
+        'Store_Location_City_Type': store_city,
+        'Store_Type': store_type,
+        'Store_Id': store_id,
+        'Store_Age': store_age
+    }])
+    
+    st.success(f"Predicted Sales: {round(prediction, 2)}")