Create app.py

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+import pyscipopt
+
+def knapsack_optimization(values, weights, capacity):
+    n = len(values)
+    model = pyscipopt.Model("Knapsack")
+
+    # Define decision variables
+    x = {i: model.addVar(f"x_{i}", vtype="B") for i in range(n)}
+
+    # Objective: Maximize total value
+    model.setObjective(sum(values[i] * x[i] for i in range(n)), "maximize")
+
+    # Constraint: Total weight should not exceed capacity
+    model.addCons(sum(weights[i] * x[i] for i in range(n)) <= capacity)
+
+    # Solve the problem
+    model.optimize()
+
+    # Get results
+    selected_items = [i for i in range(n) if model.getVal(x[i]) > 0.5]
+    total_value = sum(values[i] for i in selected_items)
+    total_weight = sum(weights[i] for i in selected_items)
+
+    return f"Optimal Value: {total_value}", f"Total Weight: {total_weight}", f"Selected Items: {selected_items}"
+
+# Define the UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 📦 Knapsack Optimization with SCIP")
+    
+    with gr.Row():
+        values_input = gr.Textbox(label="Item Values (comma-separated)", placeholder="10, 40, 30, 50")
+        weights_input = gr.Textbox(label="Item Weights (comma-separated)", placeholder="5, 8, 3, 6")
+    
+    capacity_input = gr.Number(label="Knapsack Capacity", value=10)
+    submit_button = gr.Button("Optimize")
+
+    output1 = gr.Textbox(label="Optimal Value")
+    output2 = gr.Textbox(label="Total Weight")
+    output3 = gr.Textbox(label="Selected Items")
+
+    submit_button.click(
+        fn=lambda v, w, c: knapsack_optimization(
+            list(map(int, v.split(","))), list(map(int, w.split(","))), int(c)
+        ),
+        inputs=[values_input, weights_input, capacity_input],
+        outputs=[output1, output2, output3]
+    )
+
+# Run the Gradio app
+demo.launch()