Upload app.py

app.py

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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