Add Streamlit prediction app

requirements.txt

@@ -8,6 +8,7 @@ mlflow>=2.9
 fastapi>=0.103
 uvicorn>=0.23
 jinja2>=3.1
+streamlit>=1.26
 
 # Dev
 pytest>=7.4

src/streamlit_app.py

@@ -0,0 +1,79 @@
+import pandas as pd
+import streamlit as st
+
+from predict_pipeline import predict
+
+
+st.set_page_config(page_title="Inventory Demand Forecast", layout="centered")
+
+st.title("Inventory Demand Forecast")
+st.write("Enter inputs to predict daily units sold.")
+
+with st.form("prediction_form"):
+    col1, col2 = st.columns(2)
+
+    with col1:
+        store_id = st.text_input("Store ID", value="S001")
+        product_id = st.text_input("Product ID", value="P001")
+        category = st.selectbox(
+            "Category",
+            ["Electronics", "Clothing", "Home", "Groceries", "Toys"],
+        )
+        region = st.selectbox("Region", ["North", "South", "East", "West"])
+        inventory_level = st.number_input("Inventory Level", min_value=0, value=120)
+        units_ordered = st.number_input("Units Ordered", min_value=0, value=80)
+        demand_forecast = st.number_input("Demand Forecast", min_value=0.0, value=140.5)
+        price = st.number_input("Price", min_value=0.0, value=49.99)
+
+    with col2:
+        discount = st.number_input("Discount (%)", min_value=0, value=10)
+        weather = st.selectbox(
+            "Weather Condition",
+            ["Sunny", "Rainy", "Cloudy", "Snowy"],
+        )
+        holiday = st.selectbox("Holiday/Promotion", [0, 1])
+        competitor_pricing = st.number_input(
+            "Competitor Pricing",
+            min_value=0.0,
+            value=52.0,
+        )
+        seasonality = st.selectbox(
+            "Seasonality",
+            ["Spring", "Summer", "Autumn", "Winter"],
+        )
+        day_of_week = st.number_input("Day of Week (0=Mon)", min_value=0, max_value=6, value=2)
+        month = st.number_input("Month", min_value=1, max_value=12, value=7)
+        day = st.number_input("Day", min_value=1, max_value=31, value=15)
+        is_weekend = st.selectbox("Is Weekend", [0, 1])
+
+    submitted = st.form_submit_button("Predict Units Sold")
+
+if submitted:
+    try:
+        payload = {
+            "Store ID": store_id,
+            "Product ID": product_id,
+            "Category": category,
+            "Region": region,
+            "Inventory Level": int(inventory_level),
+            "Units Ordered": int(units_ordered),
+            "Demand Forecast": float(demand_forecast),
+            "Price": float(price),
+            "Discount": int(discount),
+            "Weather Condition": weather,
+            "Holiday/Promotion": int(holiday),
+            "Competitor Pricing": float(competitor_pricing),
+            "Seasonality": seasonality,
+            "day_of_week": int(day_of_week),
+            "month": int(month),
+            "day": int(day),
+            "is_weekend": int(is_weekend),
+        }
+
+        df = pd.DataFrame([payload])
+        prediction = float(predict(df)[0])
+        st.success(f"Predicted Units Sold: {prediction:.2f}")
+    except FileNotFoundError:
+        st.error("Model not found. Train the model first.")
+    except Exception as exc:
+        st.error(f"Prediction failed: {exc}")