Initial commit

README.md

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+---
+title: Real-Time Inventory Advisor
+emoji: 📦
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.7.1
+app_file: app.py
+pinned: false
+---
+
+# Real-Time Inventory Management System
+
+This app predicts upcoming sales for each product and generates restocking advice powered by Machine Learning and Generative AI.
+
+## Features
+- Product-specific sales prediction
+- AI-powered restocking recommendations
+- Real-time inventory management
+- User-friendly interface
+
+## How to Use
+1. Enter Product ID
+2. Input Current Inventory
+3. Provide Yesterday's Sales
+4. Get AI-generated restocking advice

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+from transformers import pipeline
+
+# Load all ML models
+product_models = joblib.load('models/inventory_forecaster.pkl')
+llm = pipeline("text2text-generation", model="google/flan-t5-base")
+
+# Function to predict and generate restocking advice
+def inventory_advisor(product_id, current_inventory, last_day_sales):
+    # Select correct model
+    if product_id not in product_models:
+        return f"❌ Error: Product ID {product_id} not found in models."
+
+    forecast_model = product_models[product_id]
+    future_sales = forecast_model.predict([[last_day_sales]])[0]
+
+    prompt = (f"Current inventory is {current_inventory} units. "
+              f"Predicted sales for next week is {int(future_sales)} units. "
+              f"Should restocking be done? Suggest a human-readable restocking advice.")
+
+    response = llm(prompt, max_length=100)[0]['generated_text']
+
+    return f"🔮 Predicted Sales Next Week: {int(future_sales)} units\n\n🛒 Advice:\n{response}"
+
+iface = gr.Interface(
+    fn=inventory_advisor,
+    inputs=[
+        gr.Number(label="Product ID"),
+        gr.Number(label="Current Inventory"),
+        gr.Number(label="Units Sold Yesterday")
+    ],
+    outputs="text",
+    title="📦 Real-Time Inventory Management (Multi-Product)",
+    description="Enter product ID, current stock, and yesterday's sales. Get AI-based restocking advice!"
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+pandas
+scikit-learn
+joblib
+transformers
+torch

train_model.py

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+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import joblib
+import os
+
+os.makedirs('models', exist_ok=True)
+os.makedirs('data', exist_ok=True)
+
+data = pd.read_csv('data/sales_data_large.csv')
+
+product_models = {}
+
+for product_id, group in data.groupby('Product_ID'):
+    group = group.sort_values('Date')
+    X = group[['Units_Sold']].shift(1).fillna(0)
+    y = group['Units_Sold']
+    
+    model = LinearRegression()
+    model.fit(X, y)
+    
+    product_models[product_id] = model
+
+joblib.dump(product_models, 'models/inventory_forecaster.pkl')
+
+print(f"✅ Trained and saved models for {len(product_models)} products!")