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database.py

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+from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+
+Base = declarative_base()  # Define base class for ORM models
+
+class Product(Base):
+    __tablename__ = "products"
+    id = Column(Integer, primary_key=True)  # Primary key for products
+    name = Column(String, nullable=False)
+    current_stock = Column(Float, nullable=False)
+    safety_stock = Column(Float, nullable=False)
+    lead_time = Column(Float, nullable=False)
+    monthly_demand = Column(Float, nullable=False)
+    product_class = Column(String, nullable=False)
+
+class Transaction(Base):
+    __tablename__ = "transactions"
+    id = Column(Integer, primary_key=True)  # Primary key for transactions
+    product_id = Column(Integer, nullable=False)
+    quantity = Column(Float, nullable=False)
+    transaction_type = Column(String, nullable=False)
+    timestamp = Column(DateTime, server_default="CURRENT_TIMESTAMP")
+
+engine = create_engine("sqlite:///:memory:")  # In-memory SQLite database
+SessionLocal = sessionmaker(bind=engine)  # Session factory for database access
+
+def initialize_database():
+    Base.metadata.create_all(engine)  # Create all tables
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()  # Ensure database session is closed

inventory.py

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+from sqlalchemy.orm import Session
+from database import Product, Transaction
+import uuid
+from datetime import datetime
+
+def add_product(db: Session, name: str, current_stock: float, safety_stock: float, lead_time: float, monthly_demand: float, product_class: str):
+    product = Product(name=name, current_stock=current_stock, safety_stock=safety_stock, lead_time=lead_time, monthly_demand=monthly_demand, product_class=product_class)  # Create new product
+    db.add(product)
+    db.commit()
+    db.refresh(product)
+    return product
+
+def get_all_products(db: Session):
+    return db.query(Product).all()  # Fetch all products
+
+def get_product_by_id(db: Session, product_id: int):
+    product = db.query(Product).filter(Product.id == product_id).first()
+    if not product:
+        raise ValueError("Product not found")
+    return product
+
+# In-memory session-based functions
+def add_product_memory(session_products: list, name: str, current_stock: float, safety_stock: float, lead_time: float, monthly_demand: float, product_class: str):
+    if any(p["name"].lower() == name.lower() for p in session_products):
+        raise ValueError("Product name already exists in your session")
+    product = {"id": str(uuid.uuid4()), "name": name, "current_stock": current_stock, "safety_stock": safety_stock, "lead_time": lead_time, "monthly_demand": monthly_demand, "product_class": product_class}  # Create new in-memory product
+    session_products.append(product)
+    return product
+
+def update_product_memory(session_products: list, product_id: str, name: str, current_stock: float, safety_stock: float, lead_time: float, monthly_demand: float, product_class: str):
+    if any(p["name"].lower() == name.lower() and p["id"] != product_id for p in session_products):
+        raise ValueError("Product name already exists in your session")
+    for product in session_products:
+        if product["id"] == product_id:
+            product.update({"name": name, "current_stock": current_stock, "safety_stock": safety_stock, "lead_time": lead_time, "monthly_demand": monthly_demand, "product_class": product_class})
+            return product
+    raise ValueError("Product not found")
+
+def delete_product_memory(session_products: list, product_id: str):
+    for i, product in enumerate(session_products):
+        if product["id"] == product_id:
+            session_products.pop(i)
+            return
+    raise ValueError("Product not found")
+
+def add_stock_memory(session_products: list, session_transactions: list, product_id: str, quantity: float):
+    for product in session_products:
+        if product["id"] == product_id:
+            product["current_stock"] += quantity
+            session_transactions.append({"id": str(uuid.uuid4()), "product_id": product_id, "quantity": quantity, "transaction_type": "ADD", "timestamp": datetime.now()})  # Record stock addition
+            return
+    raise ValueError("Product not found")
+
+def remove_stock_memory(session_products: list, session_transactions: list, product_id: str, quantity: float):
+    for product in session_products:
+        if product["id"] == product_id:
+            if product["current_stock"] < quantity:
+                raise ValueError("Insufficient stock")
+            product["current_stock"] -= quantity
+            session_transactions.append({"id": str(uuid.uuid4()), "product_id": product_id, "quantity": quantity, "transaction_type": "REMOVE", "timestamp": datetime.now()})  # Record stock removal
+            return
+    raise ValueError("Product not found")
+
+def get_transactions_memory(session_transactions: list):
+    return session_transactions  # Fetch all transactions

inventory_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e4adb914d0431d5874a0caa2d83ba4abc1a487082dcd39abfc7fb4a396bf878f
+size 10678545

prediction.py

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+import joblib
+import pandas as pd
+import numpy as np
+from scipy.stats import poisson
+
+model = joblib.load("inventory_model.pkl")  # Load trained model
+
+INDUSTRIAL_PARAMS = {
+    "service_levels": {"A": 2.33, "B": 1.65, "C": 1.28},
+    "risk_thresholds": {"Low": 15, "Medium": 40, "High": 100}
+}
+
+def predict_stockout(order_quantity: float, lead_time: float, safety_stock: float, product_class: str) -> str:
+    if not (0 <= order_quantity <= 10000 and 0 <= lead_time <= 90 and 0 <= safety_stock <= 10000):
+        return "Error: Inputs out of range (Demand: 0-10,000, Lead Time: 0-90 days, Safety Stock: 0-10,000)."
+    if any(x < 0 for x in [order_quantity, lead_time, safety_stock]):
+        return "Error: Negative values not allowed."
+    if product_class not in INDUSTRIAL_PARAMS["service_levels"]:
+        return "Error: Invalid product class. Use A, B, or C."
+
+    daily_demand = order_quantity / 30
+    expected_demand = daily_demand * lead_time
+    poisson_risk = max(0, min(1, 1 - poisson.cdf(safety_stock, max(1, expected_demand))))
+    
+    safety_stock_impact = safety_stock / (order_quantity + 1e-6)
+    lead_time_impact = lead_time / 90
+    order_quantity_impact = order_quantity / 10000
+    
+    ml_input = pd.DataFrame({"safety_stock_impact": [safety_stock_impact], "lead_time_impact": [lead_time_impact], "order_quantity_impact": [order_quantity_impact]})
+    ml_risk = max(0, min(1, model.predict(ml_input)[0]))
+    
+    weight_poisson = min(1, max(0, 1 - (order_quantity / 1000)))
+    weight_ml = 1 - weight_poisson
+    blended_risk = (weight_poisson * poisson_risk + weight_ml * ml_risk)
+    risk_percentage = np.clip(blended_risk * 100, 0, 100)
+    
+    thresholds = INDUSTRIAL_PARAMS["risk_thresholds"]
+    if risk_percentage < thresholds["Low"]:
+        return f"<div class='risk-low'>Low Risk (Class {product_class})<br>Risk: {risk_percentage:.1f}%<br>No action needed</div>"
+    elif risk_percentage < thresholds["Medium"]:
+        return f"<div class='risk-medium'>Medium Risk (Class {product_class})<br>Risk: {risk_percentage:.1f}%<br>Monitor closely</div>"
+    else:
+        return f"<div class='risk-high'>High Risk (Class {product_class})<br>Risk: {risk_percentage:.1f}%<br>Replenish inventory</div>"  # Return risk prediction HTML

requirements.txt

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+streamlit==1.38.0
+pandas==2.1.4
+scikit-learn==1.3.2
+joblib==1.3.2
+numpy==1.26.0
+scipy==1.11.4
+sqlalchemy==2.0.35

styles.css

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+.risk-low { color: #28a745; font-weight: bold; }
+.risk-medium { color: #ffc107; font-weight: bold; }
+.risk-high { color: #dc3545; font-weight: bold; }
+.alert-high { color: #dc3545; font-weight: bold; border: 2px solid #dc3545; padding: 12px; margin: 8px; background-color: #ffe6e6; }
+.alert-medium { color: #ffc107; font-weight: bold; border: 2px solid #ffc107; padding: 12px; margin: 8px; background-color: #fff3cd; }
+.footer { font-size: small; color: grey; text-align: center; margin-top: 20px; }