Update app.py

app.py

@@ -1,108 +1,214 @@
 import gradio as gr
-import json
-import pandas as pd
-from datetime import datetime
 
-# Load products
-with open("products.json", "r") as f:
-    products = json.load(f)
+# ------------------------------
+# PRODUCT DATABASE
+# ------------------------------
+
+products = [
+    {
+        "name": "Rice",
+        "brand": "India Gate",
+        "category": "Groceries",
+        "price": 60,
+        "unit": "kg",
+        "stock": 100,
+        "image": "https://images.unsplash.com/photo-1586201375761-83865001e31c"
+    },
+    {
+        "name": "Milk",
+        "brand": "Heritage",
+        "category": "Groceries",
+        "price": 28,
+        "unit": "liter",
+        "stock": 50,
+        "image": "https://images.unsplash.com/photo-1582719478250-c89cae4dc85b"
+    },
+    {
+        "name": "Bath Soap",
+        "brand": "Lux",
+        "category": "Personal Care",
+        "price": 35,
+        "unit": "piece",
+        "stock": 80,
+        "image": "https://images.unsplash.com/photo-1600857544200-b2f666a9a2ec"
+    },
+    {
+        "name": "Shampoo",
+        "brand": "Clinic Plus",
+        "category": "Personal Care",
+        "price": 120,
+        "unit": "bottle",
+        "stock": 40,
+        "image": "https://images.unsplash.com/photo-1620916566398-39f1143ab7be"
+    },
+    {
+        "name": "Detergent Powder",
+        "brand": "Surf Excel",
+        "category": "Cleaning",
+        "price": 95,
+        "unit": "kg",
+        "stock": 60,
+        "image": "https://images.unsplash.com/photo-1615486511484-92e172cc4fe0"
+    }
+]
 
 cart = []
 
-def filter_products(category):
-    return [
-        f"{p['id']} - {p['name']} ({p['brand']}) | ₹{p['price']} per {p['unit']} | Stock: {p['stock']}"
-        for p in products if p["category"] == category
-    ]
+# ------------------------------
+# AI RECOMMENDATION RULES
+# ------------------------------
+
+recommendation_rules = {
+    "Rice": ["Milk"],
+    "Milk": ["Rice"],
+    "Bath Soap": ["Shampoo"],
+    "Shampoo": ["Bath Soap"],
+    "Detergent Powder": ["Bath Soap"]
+}
+
+# ------------------------------
+# SEARCH FUNCTION
+# ------------------------------
+
+def search_products(query, category):
+    results = []
+    for product in products:
+        if (query.lower() in product["name"].lower()) and \
+           (category == "All" or product["category"] == category):
+
+            results.append(
+                f"""
+### {product['name']} ({product['brand']})
+Price: ₹{product['price']} per {product['unit']}
+Stock: {product['stock']} available
+![image]({product['image']})
+"""
+            )
+    return "\n".join(results) if results else "No products found."
+
+# ------------------------------
+# ADD TO CART
+# ------------------------------
+
+def add_to_cart(product_name, quantity):
+    for product in products:
+        if product["name"] == product_name:
+            if quantity <= product["stock"]:
+                total = quantity * product["price"]
+                cart.append({
+                    "Product": product_name,
+                    "Quantity": quantity,
+                    "Price": product["price"],
+                    "Total": total
+                })
+                product["stock"] -= quantity
+                return f"Added {quantity} {product['unit']} of {product_name} to cart."
+            else:
+                return "Not enough stock available."
+    return "Product not found."
+
+# ------------------------------
+# VIEW CART
+# ------------------------------
 
-def add_to_cart(product_id, quantity):
-    for p in products:
-        if p["id"] == int(product_id):
-            if quantity > p["stock"]:
-                return "Not enough stock available!"
+def view_cart():
+    if not cart:
+        return "Cart is empty."
 
-            total = quantity * p["price"]
+    bill = "🧾 BILL DETAILS\n\n"
+    grand_total = 0
 
-            cart.append({
-                "Product": p["name"],
-                "Brand": p["brand"],
-                "Quantity": quantity,
-                "Unit": p["unit"],
-                "Price": p["price"],
-                "Total": total
-            })
+    for item in cart:
+        bill += f"{item['Product']} x {item['Quantity']} = ₹{item['Total']}\n"
+        grand_total += item["Total"]
 
-            p["stock"] -= quantity
-            return f"{p['name']} added to cart!"
+    bill += f"\nTotal Amount: ₹{grand_total}"
+    return bill
 
-    return "Product not found!"
+# ------------------------------
+# AI RECOMMENDATION
+# ------------------------------
 
-def view_cart():
+def recommend_products():
     if not cart:
-        return pd.DataFrame()
+        return "Add items to cart to see recommendations."
 
-    return pd.DataFrame(cart)
-
-def generate_bill():
-    if not cart:
-        return "Cart is empty!"
-
-    df = pd.DataFrame(cart)
-    grand_total = df["Total"].sum()
-
-    bill_text = "🧾 Supermarket Bill\n"
-    bill_text += "---------------------------------\n"
+    recommended = set()
 
     for item in cart:
-        bill_text += f"{item['Product']} - {item['Quantity']} {item['Unit']} = ₹{item['Total']}\n"
+        product_name = item["Product"]
+        if product_name in recommendation_rules:
+            for suggestion in recommendation_rules[product_name]:
+                recommended.add(suggestion)
 
-    bill_text += "---------------------------------\n"
-    bill_text += f"Grand Total: ₹{grand_total}\n"
-    bill_text += f"Date: {datetime.now()}\n"
+    suggestion_text = "🧠 Recommended Products:\n"
+    for product in products:
+        if product["name"] in recommended:
+            suggestion_text += f"- {product['name']} ({product['brand']}) | ₹{product['price']} per {product['unit']}\n"
 
-    return bill_text
+    return suggestion_text
 
+# ------------------------------
+# GRADIO UI
+# ------------------------------
 
 with gr.Blocks() as demo:
-    gr.Markdown("# 🛒 Smart Supermarket System")
 
-    category = gr.Dropdown(
-        choices=["Groceries", "Personal Care", "Cleaning"],
-        label="Select Category"
-    )
+    gr.Markdown("# 🛒 AI Supermarket App")
 
-    product_list = gr.Dropdown(label="Products")
+    with gr.Row():
+        search_box = gr.Textbox(label="Search Product")
+        category_filter = gr.Dropdown(
+            choices=["All", "Groceries", "Personal Care", "Cleaning"],
+            value="All",
+            label="Category"
+        )
 
-    quantity = gr.Number(label="Quantity", value=1)
+    search_output = gr.Markdown()
 
-    add_btn = gr.Button("Add to Cart")
+    search_btn = gr.Button("🔍 Search")
+    search_btn.click(
+        search_products,
+        inputs=[search_box, category_filter],
+        outputs=search_output
+    )
 
-    cart_table = gr.Dataframe(label="Cart")
+    gr.Markdown("## 🛒 Add to Cart")
 
-    bill_output = gr.Textbox(label="Generated Bill")
+    product_dropdown = gr.Dropdown(
+        choices=[p["name"] for p in products],
+        label="Select Product"
+    )
 
-    bill_btn = gr.Button("Generate Bill")
+    quantity_input = gr.Number(label="Quantity", value=1)
 
-    category.change(
-        lambda x: filter_products(x),
-        inputs=category,
-        outputs=product_list
-    )
+    add_btn = gr.Button("➕ Add to Cart")
+    add_output = gr.Textbox()
 
     add_btn.click(
         add_to_cart,
-        inputs=[product_list, quantity],
-        outputs=bill_output
+        inputs=[product_dropdown, quantity_input],
+        outputs=add_output
     )
 
-    add_btn.click(
+    gr.Markdown("## 🧾 View Bill")
+
+    cart_btn = gr.Button("🧾 Generate Bill")
+    cart_output = gr.Textbox()
+
+    cart_btn.click(
         view_cart,
-        outputs=cart_table
+        outputs=cart_output
     )
 
-    bill_btn.click(
-        generate_bill,
-        outputs=bill_output
+    gr.Markdown("## 🧠 AI Recommendations")
+
+    recommend_btn = gr.Button("🧠 Get Recommendations")
+    recommend_output = gr.Textbox()
+
+    recommend_btn.click(
+        recommend_products,
+        outputs=recommend_output
     )
 
 demo.launch()