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| import streamlit as st | |
| import pandas as pd | |
| # Streamlit app title and description | |
| st.title("AI-Powered Inventory Management System") | |
| st.write(""" | |
| This proof of concept demonstrates how IBM Watson and IBM Granite can be used to optimize retail inventory management. | |
| Upload synthetic data to get AI-driven insights. | |
| """) | |
| # Step 1: Upload synthetic data files | |
| st.header("Upload Your Inventory Data") | |
| uploaded_file = st.file_uploader("Choose a CSV file", type="csv") | |
| if uploaded_file is not None: | |
| # Step 2: Preview the uploaded data | |
| data = pd.read_csv(uploaded_file) | |
| st.subheader("Preview of the uploaded data:") | |
| st.dataframe(data.head()) | |
| # Step 3: Call Watson and Granite APIs for AI processing (Placeholder functions) | |
| if st.button("Generate AI Insights"): | |
| st.subheader("AI-Powered Insights") | |
| # Placeholder for Watson API call | |
| demand_forecast = call_watson_api(data) | |
| st.write("Demand Forecasting:") | |
| st.write(demand_forecast) | |
| # Placeholder for Granite API call | |
| insights = call_granite_api(data) | |
| st.write("AI-Generated Recommendations:") | |
| st.write(insights) | |
| # Placeholder function for Watson API integration | |
| def call_watson_api(data): | |
| # Simulated AI output | |
| demand_forecast = { | |
| "Product A": "Reorder in 5 days", | |
| "Product B": "Stock sufficient for 10 days", | |
| } | |
| return demand_forecast | |
| # Placeholder function for Granite API integration | |
| def call_granite_api(data): | |
| # Simulated AI output | |
| insights = { | |
| "Recommendation": "Run a promotion for Product C to clear overstock", | |
| "Reorder Alert": "Product D needs restocking in 3 days", | |
| } | |
| return insights | |