feat: Launched Streamlit web dashboard v1.0

Author: sandesh-s-hegde

app.py

@@ -0,0 +1,72 @@
+"""
+app.py
+The Web Interface for the Digital Capacity Optimizer.
+Run with: streamlit run app.py
+"""
+import streamlit as st
+import pandas as pd
+from inventory_math import calculate_newsvendor_target, calculate_required_inventory
+from forecaster import generate_forecast
+from visualizer import plot_forecast
+import config
+
+# --- 1. Page Configuration ---
+st.set_page_config(page_title="Digital Capacity Optimizer", page_icon="🏭")
+
+st.title("🏭 Digital Capacity Optimizer")
+st.markdown("""
+**AI-Powered Supply Chain Planning** Upload your demand logs to generate forecasts and optimized procurement plans.
+""")
+
+# --- 2. Sidebar (Settings) ---
+st.sidebar.header("⚙️ Configuration")
+holding_cost = st.sidebar.number_input("Holding Cost ($/Unit/Year)", value=config.HOLDING_COST)
+stockout_cost = st.sidebar.number_input("Stockout Cost ($/Unit)", value=config.STOCKOUT_COST)
+
+# --- 3. File Upload ---
+uploaded_file = st.file_uploader("Upload CSV Usage Logs", type=["csv"])
+
+if uploaded_file is not None:
+    # Load Data
+    df = pd.read_csv(uploaded_file)
+    st.success(f"✅ Loaded {len(df)} months of data.")
+
+    # Show Raw Data (Expandable)
+    with st.expander("View Raw Data"):
+        st.dataframe(df)
+
+    # --- 4. Run Forecasting Engine ---
+    st.subheader("🔮 Demand Forecast")
+
+    months_to_predict = st.slider("Months to Forecast", min_value=1, max_value=6, value=3)
+
+    with st.spinner("Training AI Model..."):
+        forecast_result = generate_forecast(df, months_ahead=months_to_predict)
+        forecast_values = forecast_result['forecast_values']
+
+        # Display Chart
+        fig = plot_forecast(df, forecast_values)
+        st.pyplot(fig)
+
+    # --- 5. Optimization Results ---
+    st.subheader("📦 Procurement Recommendation")
+
+    # Get Next Month's Prediction
+    next_month_demand = forecast_values.iloc[0]
+    std_dev_demand = df['demand'].std()
+
+    # Calculate Math
+    optimal_sla = calculate_newsvendor_target(holding_cost, stockout_cost)
+    required_inventory = calculate_required_inventory(next_month_demand, std_dev_demand, optimal_sla)
+
+    # Display Key Metrics in Columns
+    col1, col2, col3 = st.columns(3)
+    col1.metric("Predicted Demand", f"{int(next_month_demand)} units", delta="Next Month")
+    col2.metric("Optimal Service Level", f"{optimal_sla * 100:.1f}%", help="Based on Cost Ratio")
+    col3.metric("Recommended Order", f"{int(required_inventory)} units", delta="Final Decision")
+
+    st.info(
+        f"💡 **Insight:** To maintain a {optimal_sla * 100:.1f}% Service Level with current volatility, you need **{int(required_inventory)} units** in stock.")
+
+else:
+    st.info("👆 Please upload a CSV file to begin simulation. (Use 'mock_data.csv')")