add examples

app.py

@@ -1,133 +1,149 @@
-import gradio as gr
-import numpy as np
-import pulp
-
-
-def bounded_knapsack_problem_solver(target, df):
-
-    name_ls = []
-    for index, item in enumerate(df["name"]):
-        if item:
-            name_ls.append(item)
-        else:
-            name_ls.append(f"📦{str(index+1)}")
-
-    if len(name_ls) != len(set(name_ls)):
-        return "Please check for duplicate item names and try again."
-
-    items = np.array(name_ls)  # 物品名称
-    prices = np.array([int(p) if p else 0 for p in df["value"]])  # 物品单价
-    stocks = np.array([int(c) if c else 0 for c in df["count"]])  # 物品数量
-
-    # 创建问题实例，最大化物品总价值
-    prob = pulp.LpProblem("Maximize_Value", pulp.LpMaximize)
-
-    # 决策变量，装入背包的物品物品组合，件数必须是整数
-    x = pulp.LpVariable.dicts("x", range(len(prices)), lowBound=0, cat="Integer")
-
-    # 遍历，设置决策变量的上界为库存量
-    for i in range(len(stocks)):
-        x[i].upBound = stocks[i]
-
-    # 目标函数：最大化物品总价值
-    prob += pulp.lpSum([prices[i] * x[i] for i in range(len(stocks))])
-
-    # 添加约束条件： 总价值小于等于目标值
-    prob += pulp.lpSum([prices[i] * x[i] for i in range(len(stocks))]) <= target
-
-    # 求解问题
-    # CBC(Coin-or Branch and Cut)求解器使用分支定界算法来寻找整数规划问题的最优解
-    solver = pulp.getSolver("PULP_CBC_CMD")
-    prob.solve(solver=solver)
-
-    if pulp.LpStatus[prob.status] == "Optimal":
-
-        value = 0
-        solution = []
-        for i, v in x.items():
-            if v.varValue:
-                if v.varValue > 0:
-                    solution.append(f"{items[i]}(${prices[i]}): {int(v.varValue)}\n")
-                    value += int(v.varValue) * prices[i]
-        if value == target:
-            return f"Optimal solution found:\n\n{''.join(solution)}\nTotal value: {value}\nGreat! Found a combination of items with a total value equal to the target value(${target}).😃"
-
-        else:
-            return f"Optimal solution found:\n\n{''.join(solution)}\nTotal value: {value}\n${target - value} short of the target value(${target}).😂"
-
-
-# Create a Gradio interface with a column layout
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        <center><font size=8>📦Easy Bounded Knapsack Problem Solver📦</center>
-
-        ### Given
-
-            - A set of items, each with:
-                - Value ( v_i )
-                - Maximum quantity ( q_i )
-            - A target value ( T )
-
-        ### Objective
-
-            Find a combination of items such that the sum of the selected items' values equals ( T ) 
-            or maximizes the total value without exceeding ( T ). 
-            Each item ( i ) can be selected up to ( q_i ) times.
-        """
-    )
-
-    with gr.Column():
-        # Add a row for the target input
-        target = gr.Number(
-            label="Target",
-            info="number less than 50,000",
-            value=0,
-            precision=0,
-            minimum=0,
-            maximum=50000,
-            step=100,
-        )
-
-        df = gr.Dataframe(
-            headers=["name", "value", "count"],
-            datatype=["str", "number", "number"],
-            row_count=3,
-            col_count=(3, "fixed"),
-            label="Items",
-        )
-
-        # Add a radio selection for the strategy
-        # condition = gr.Radio(
-        #     [
-        #         ("最小化物品数量(优先选择高价物品)", "MinimizeCount"),
-        #         ("最大化物品数量(优先选择低价物品)", "MaximizeCount"),
-        #     ],
-        #     value="MinimizeCount",
-        #     label="Condition",
-        # )
-
-        # Add a row for the Clear and Calculate buttons
-        with gr.Row():
-            clear_btn = gr.ClearButton(df, size="sm", value="❌Clear")
-
-            # Add a button to trigger the calculation
-            submit_btn = gr.Button(value="🛠Calculate")
-
-        # Add a row for the result textbox
-        with gr.Row():
-            result = gr.Textbox(label="Output")
-
-    # Set up the button click event to call the calculator function
-    submit_btn.click(
-        bounded_knapsack_problem_solver,
-        inputs=[target, df],
-        outputs=[result],
-    )
-
-# Launch the Gradio application
-demo.queue(api_open=False)
-demo.launch(max_threads=5, share=False)
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import numpy as np
+import pulp
+
+
+def bounded_knapsack_problem_solver(target, df):
+
+    name_ls = []
+    for index, item in enumerate(df["name"]):
+        if item:
+            name_ls.append(item)
+        else:
+            name_ls.append(f"📦{str(index+1)}")
+
+    if len(name_ls) != len(set(name_ls)):
+        return "Please check for duplicate item names and try again."
+
+    items = np.array(name_ls)  # 物品名称
+    prices = np.array([int(p) if p else 0 for p in df["value"]])  # 物品单价
+    stocks = np.array([int(c) if c else 0 for c in df["count"]])  # 物品数量
+
+    # 创建问题实例，最大化物品总价值
+    prob = pulp.LpProblem("Maximize_Value", pulp.LpMaximize)
+
+    # 决策变量，装入背包的物品物品组合，件数必须是整数
+    x = pulp.LpVariable.dicts("x", range(len(prices)), lowBound=0, cat="Integer")
+
+    # 遍历，设置决策变量的上界为库存量
+    for i in range(len(stocks)):
+        x[i].upBound = stocks[i]
+
+    # 目标函数：最大化物品总价值
+    prob += pulp.lpSum([prices[i] * x[i] for i in range(len(stocks))])
+
+    # 添加约束条件： 总价值小于等于目标值
+    prob += pulp.lpSum([prices[i] * x[i] for i in range(len(stocks))]) <= target
+
+    # 求解问题
+    # CBC(Coin-or Branch and Cut)求解器使用分支定界算法来寻找整数规划问题的最优解
+    solver = pulp.getSolver("PULP_CBC_CMD")
+    prob.solve(solver=solver)
+
+    if pulp.LpStatus[prob.status] == "Optimal":
+
+        value = 0
+        solution = []
+        for i, v in x.items():
+            if v.varValue:
+                if v.varValue > 0:
+                    solution.append(f"{items[i]}(${prices[i]}): {int(v.varValue)}\n")
+                    value += int(v.varValue) * prices[i]
+        if value == target:
+            return f"Optimal solution found:\n\n{''.join(solution)}\nTotal value: {value}\nGreat! Found a combination of items with a total value equal to the target value(${target}).😃"
+
+        else:
+            return f"Optimal solution found:\n\n{''.join(solution)}\nTotal value: {value}\n${target - value} short of the target value(${target}).😂"
+
+
+# Create a Gradio interface with a column layout
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        <center><font size=8>📦Easy Bounded Knapsack Problem Solver📦</center>
+
+        ### Given
+
+            - A set of items, each with:
+                - Value ( v_i )
+                - Maximum quantity ( q_i )
+            - A target value ( T )
+
+        ### Objective
+
+            Find a combination of items such that the sum of the selected items' values equals ( T ) 
+            or maximizes the total value without exceeding ( T ). 
+            Each item ( i ) can be selected up to ( q_i ) times.
+        """
+    )
+
+    with gr.Column():
+        # Add a row for the target input
+        target = gr.Number(
+            label="Target",
+            info="number less than 50,000",
+            value=0,
+            precision=0,
+            minimum=0,
+            maximum=50000,
+            step=100,
+        )
+
+        df = gr.Dataframe(
+            headers=["name", "value", "count"],
+            datatype=["str", "number", "number"],
+            row_count=3,
+            col_count=(3, "fixed"),
+            label="Items",
+        )
+
+        # Add a radio selection for the strategy
+        # condition = gr.Radio(
+        #     [
+        #         ("最小化物品数量(优先选择高价物品)", "MinimizeCount"),
+        #         ("最大化物品数量(优先选择低价物品)", "MaximizeCount"),
+        #     ],
+        #     value="MinimizeCount",
+        #     label="Condition",
+        # )
+
+        # Add a row for the Clear and Calculate buttons
+        with gr.Row():
+            clear_btn = gr.ClearButton(df, size="sm", value="❌Clear")
+
+            # Add a button to trigger the calculation
+            submit_btn = gr.Button(value="🛠Calculate")
+
+        # Add a row for the result textbox
+        with gr.Row():
+            result = gr.Textbox(label="Output")
+    
+    examples = gr.Examples(
+        examples=[
+            [150, [["", 12, 10], ["", 9, 5], ["", 8, 10]]],
+            [
+                500,
+                [
+                    ["pencil", 129, 1],
+                    ["magic keyboard", 349, 1],
+                    ["homepod", 299, 1],
+                    ["airpods max", 549, 1],
+                ],
+            ],
+        ],
+        inputs=[target, df],
+    )    
+    
+    # Set up the button click event to call the calculator function
+    submit_btn.click(
+        bounded_knapsack_problem_solver,
+        inputs=[target, df],
+        outputs=[result],
+    )
+
+# Launch the Gradio application
+demo.queue(api_open=False)
+demo.launch(max_threads=5, share=False)
+
+if __name__ == "__main__":
+    demo.launch()