Create app.py

app.py

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+import gradio as gr
+from tabulate import tabulate
+import random
+import numpy as np
+from sklearn.cluster import KMeans
+
+class ProductSystem:
+    def __init__(self):
+        self.product_catalog = {
+            "laptop": ["Laptop Model A", "Laptop Model B", "Laptop Model C", "Laptop Model D"],
+            "mouse": ["Wireless Mouse", "Gaming Mouse", "Ergonomic Mouse"],
+            "charger": ["Laptop Charger A", "Laptop Charger B"],
+            "keyboard": ["Mechanical Keyboard", "Wireless Keyboard"],
+            "monitor": ["24-inch Monitor", "27-inch Monitor"],
+            "printer": ["Laser Printer", "Inkjet Printer"],
+            "headphones": ["Noise-Canceling Headphones", "Bluetooth Headphones"],
+            "speakers": ["Wireless Speakers", "USB Speakers"],
+            "external_storage": ["1TB External HDD", "500GB SSD"]
+        }
+        self.stock = {
+            "Laptop Model A": 5, "Laptop Model B": 10, "Laptop Model C": 2, "Laptop Model D": 0,
+            "Wireless Mouse": 15, "Gaming Mouse": 5, "Ergonomic Mouse": 8,
+            "Laptop Charger A": 3, "Laptop Charger B": 7,
+            "Mechanical Keyboard": 6, "Wireless Keyboard": 4,
+            "24-inch Monitor": 5, "27-inch Monitor": 3,
+            "Laser Printer": 4, "Inkjet Printer": 6,
+            "Noise-Canceling Headphones": 10, "Bluetooth Headphones": 7,
+            "Wireless Speakers": 8, "USB Speakers": 9,
+            "1TB External HDD": 6, "500GB SSD": 5
+        }
+        self.resource_stock = {
+            "Processor": 20, "RAM": 30, "SSD": 15, "Battery": 10, "Screen": 8,
+            "Mouse Sensor": 25, "Keyboard Switches": 40, "Charging Cable": 12,
+            "Printer Ink": 20, "Monitor Panel": 10, "Speaker Driver": 15, "Headphone Cushions": 18, "External Storage Chips": 12,
+            "GPU": 5, "Motherboard": 7, "Cooling Fan": 10, "Power Supply": 6, "Touchpad": 8
+        }
+        self.resource_requirements = {
+            "laptop": {"Processor": 1, "RAM": 2, "SSD": 1, "Battery": 1, "Screen": 1, "GPU": 1, "Motherboard": 1, "Cooling Fan": 1, "Power Supply": 1, "Touchpad": 1},
+            "mouse": {"Mouse Sensor": 1},
+            "keyboard": {"Keyboard Switches": 10},
+            "charger": {"Charging Cable": 1},
+            "monitor": {"Monitor Panel": 1},
+            "printer": {"Printer Ink": 2},
+            "headphones": {"Headphone Cushions": 2},
+            "speakers": {"Speaker Driver": 2},
+            "external_storage": {"External Storage Chips": 1}
+        }
+        self.historical_data = np.array([
+            [1, 1, 0, 0, 1],
+            [1, 0, 1, 1, 0],
+            [1, 1, 1, 0, 1],
+            [1, 0, 0, 1, 1],
+            [1, 1, 1, 1, 1]
+        ])
+
+    def suggest_products(self, user_input):
+        primary_products = self.product_catalog.get(user_input, [])[:4]
+
+        kmeans = KMeans(n_clusters=2, random_state=42, n_init=10)
+        kmeans.fit(self.historical_data)
+        cluster = kmeans.predict(self.historical_data[-1].reshape(1, -1))[0]
+
+        recommended_secondary = []
+        for i, value in enumerate(self.historical_data[cluster]):
+            if value == 1:
+                recommended_secondary.append(list(self.product_catalog.keys())[i])
+
+        secondary_products = []
+        for product in recommended_secondary:
+            secondary_products.extend(self.product_catalog.get(product, []))
+
+        secondary_products = [prod for prod in secondary_products if prod not in primary_products]
+        secondary_products = random.sample(secondary_products, min(5, len(secondary_products)))
+
+        return primary_products, secondary_products
+
+    def check_resources(self, product_type, quantity_needed):
+        required_resources = self.resource_requirements.get(product_type, {})
+        resource_data = []
+
+        for resource, qty_per_unit in required_resources.items():
+            total_needed = qty_per_unit * quantity_needed
+            available = self.resource_stock.get(resource, 0)
+            restock_needed = max(0, total_needed - available)
+            resource_data.append([resource, available, total_needed, restock_needed])
+
+        return resource_data
+
+system = ProductSystem()
+
+def interface_main(product_type, quantity):
+    primary, secondary = system.suggest_products(product_type)
+    table_data = system.check_resources(product_type, quantity)
+    resource_table = tabulate(table_data, headers=["Resource", "Available", "Required", "Restock"], tablefmt="grid")
+    return primary, secondary, resource_table
+
+demo = gr.Interface(
+    fn=interface_main,
+    inputs=[
+        gr.Dropdown(choices=list(system.product_catalog.keys()), label="Select Primary Product Type"),
+        gr.Number(label="Enter Quantity", value=1)
+    ],
+    outputs=[
+        gr.Textbox(label="Primary Products"),
+        gr.Textbox(label="Recommended Secondary Products"),
+        gr.Textbox(label="Resource Requirements Table")
+    ],
+    title="Product Manufacturing & Recommendation System",
+    description="Suggests products and checks resource availability using AI clustering (KMeans)."
+)
+
+if __name__ == "__main__":
+    demo.launch()