Update app.py

app.py

@@ -1,527 +1,29 @@
-import gradio as gr
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-from datetime import datetime, timedelta
-import random
-import json
-
-# Simple inventory data
-inventory_data = [
-    {"id": "INV001", "name": "Wireless Headphones", "category": "Electronics", "stock": 45, "min_stock": 10, "price": 99.99},
-    {"id": "INV002", "name": "Office Chair", "category": "Furniture", "stock": 8, "min_stock": 15, "price": 299.99},
-    {"id": "INV003", "name": "Laptop Stand", "category": "Accessories", "stock": 0, "min_stock": 5, "price": 49.99},
-    {"id": "INV004", "name": "Bluetooth Speaker", "category": "Electronics", "stock": 23, "min_stock": 10, "price": 79.99},
-    {"id": "INV005", "name": "Desk Lamp", "category": "Lighting", "stock": 12, "min_stock": 8, "price": 39.99},
-]
-
-# Customer data
-customers_data = [
-    {"id": "CUST001", "name": "John Smith", "email": "john.smith@acme.com", "phone": "+1-555-0101", "company": "Acme Corporation", "total_orders": 24, "total_spent": 12450.0, "status": "Active"},
-    {"id": "CUST002", "name": "Sarah Johnson", "email": "sarah.j@techsolutions.com", "phone": "+1-555-0102", "company": "Tech Solutions Inc", "total_orders": 18, "total_spent": 8920.0, "status": "Active"},
-    {"id": "CUST003", "name": "Michael Brown", "email": "m.brown@startup.io", "phone": "+1-555-0103", "company": "Startup Innovations", "total_orders": 7, "total_spent": 3240.0, "status": "Inactive"},
-    {"id": "CUST004", "name": "Emily Davis", "email": "emily.davis@enterprise.com", "phone": "+1-555-0104", "company": "Enterprise Solutions", "total_orders": 31, "total_spent": 18750.0, "status": "VIP"},
-    {"id": "CUST005", "name": "David Wilson", "email": "d.wilson@globaltech.com", "phone": "+1-555-0105", "company": "Global Tech", "total_orders": 15, "total_spent": 7890.0, "status": "Active"},
-]
-
-# Sales data
-sales_data = [
-    {"month": "Jan", "sales": 28000, "orders": 240, "customers": 45},
-    {"month": "Feb", "sales": 32000, "orders": 280, "customers": 52},
-    {"month": "Mar", "sales": 35000, "orders": 310, "customers": 48},
-    {"month": "Apr", "sales": 38000, "orders": 340, "customers": 61},
-    {"month": "May", "sales": 42000, "orders": 380, "customers": 55},
-    {"month": "Jun", "sales": 45000, "orders": 420, "customers": 67},
-]
-
-def show_dashboard():
-    """Simple dashboard display"""
-    total_products = len(inventory_data)
-    in_stock = sum(1 for p in inventory_data if p["stock"] > p["min_stock"])
-    low_stock = sum(1 for p in inventory_data if p["stock"] <= p["min_stock"] and p["stock"] > 0)
-    out_of_stock = sum(1 for p in inventory_data if p["stock"] == 0)
-    
-    return f"""
-📦 SETA INVENTORY DASHBOARD
-
-📊 KEY METRICS:
-• Total Products: {total_products}
-• In Stock: {in_stock}
-• Low Stock: {low_stock}
-• Out of Stock: {out_of_stock}
-
-📈 RECENT ACTIVITY:
-• Stock updated: Wireless Headphones
-• Low stock alert: Office Chair
-• New order received
-• Customer registered
-
-🚨 ALERTS:
-• Office Chair: Only 8 units left (Min: 15)
-• Laptop Stand: Out of stock (Min: 5)
-"""
-
-def show_inventory():
-    """Display inventory table"""
-    data = []
-    for item in inventory_data:
-        status = "In Stock" if item["stock"] > item["min_stock"] else "Low Stock" if item["stock"] > 0 else "Out of Stock"
-        data.append([
-            item["id"],
-            item["name"],
-            item["category"],
-            item["stock"],
-            item["min_stock"],
-            f"${item['price']:.2f}",
-            status
-        ])
-    
-    df = pd.DataFrame(data, columns=["ID", "Name", "Category", "Stock", "Min Stock", "Price", "Status"])
-    return df
-
-def search_products(search_term):
-    """Search products"""
-    if not search_term:
-        return show_inventory()
-    
-    filtered = [p for p in inventory_data if search_term.lower() in p["name"].lower() or search_term.lower() in p["id"].lower()]
-    
-    data = []
-    for item in filtered:
-        status = "In Stock" if item["stock"] > item["min_stock"] else "Low Stock" if item["stock"] > 0 else "Out of Stock"
-        data.append([
-            item["id"],
-            item["name"],
-            item["category"],
-            item["stock"],
-            item["min_stock"],
-            f"${item['price']:.2f}",
-            status
-        ])
-    
-    df = pd.DataFrame(data, columns=["ID", "Name", "Category", "Stock", "Min Stock", "Price", "Status"])
-    return df
-
-def scan_product(product_input):
-    """Scan product by ID or name"""
-    if not product_input:
-        return "Please enter a product ID or name"
-    
-    for product in inventory_data:
-        if product_input.upper() in product["id"] or product_input.lower() in product["name"].lower():
-            status = "In Stock" if product["stock"] > product["min_stock"] else "Low Stock" if product["stock"] > 0 else "Out of Stock"
-            return f"""
-SCAN RESULT:
-Product: {product["name"]}
-ID: {product["id"]}
-Category: {product["category"]}
-Current Stock: {product["stock"]} units
-Minimum Stock: {product["min_stock"]} units
-Price: ${product["price"]:.2f}
-Status: {status}
-"""
-    
-    return f"No product found for: {product_input}"
-
-def add_product(product_id, name, category, stock, min_stock, price):
-    """Add new product"""
-    if not all([product_id, name, category]):
-        return "Please fill in Product ID, Name, and Category"
-    
-    if any(item["id"] == product_id for item in inventory_data):
-        return f"Product ID {product_id} already exists!"
-    
-    new_product = {
-        "id": product_id,
-        "name": name,
-        "category": category,
-        "stock": int(stock or 0),
-        "min_stock": int(min_stock or 0),
-        "price": float(price or 0)
-    }
-    
-    inventory_data.append(new_product)
-    return f"Successfully added {name} to inventory!"
-
-def update_stock_level(product_id, new_stock):
-    """Update product stock"""
-    if not product_id or new_stock is None:
-        return "Please select a product and enter stock amount"
-    
-    for item in inventory_data:
-        if item["id"] == product_id:
-            old_stock = item["stock"]
-            item["stock"] = int(new_stock)
-            return f"Updated {item['name']}: {old_stock} → {new_stock} units"
-    
-    return "Product not found"
-
-def create_sales_chart():
-    """Create sales trend chart"""
-    df = pd.DataFrame(sales_data)
-    fig = px.line(df, x='month', y=['sales', 'orders'], 
-                  title='📈 Sales & Orders Trend (6 Months)',
-                  labels={'value': 'Amount', 'variable': 'Metric'})
-    fig.update_layout(height=400)
-    return fig
-
-def create_inventory_chart():
-    """Create inventory distribution chart"""
-    categories = {}
-    for item in inventory_data:
-        cat = item['category']
-        categories[cat] = categories.get(cat, 0) + item['stock']
-    
-    df = pd.DataFrame(list(categories.items()), columns=['Category', 'Stock'])
-    fig = px.pie(df, values='Stock', names='Category', 
-                 title='📦 Inventory Distribution by Category')
-    fig.update_layout(height=400)
-    return fig
-
-def create_customer_chart():
-    """Create customer status chart"""
-    status_counts = {}
-    for customer in customers_data:
-        status = customer['status']
-        status_counts[status] = status_counts.get(status, 0) + 1
-    
-    df = pd.DataFrame(list(status_counts.items()), columns=['Status', 'Count'])
-    fig = px.bar(df, x='Status', y='Count', 
-                 title='👥 Customer Status Distribution')
-    fig.update_layout(height=400)
-    return fig
-
-def process_ocr_image(image):
-    """Process image with simulated OCR"""
-    if image is None:
-        return "📷 Please upload an image to process with OCR."
-    
-    # Simulate OCR processing
-    product_types = ["Fusion", "Agroking", "Jaguar", "Generic"]
-    selected_type = random.choice(product_types)
-    
-    if selected_type == "Fusion":
-        result = f"""
-🔍 **OCR PROCESSING COMPLETE**
-
-**Product Detected:** Fusion Pro Tablet
-**Product ID:** FUS-2024-{random.randint(100, 999)}
-**Manufacturer:** Fusion Tech
-**Price:** $349.99
-**Quantity:** 1 unit
-**Serial Number:** FT{random.randint(10000, 99999)}
-
-**Extracted Attributes:**
-• Screen Size: 10.5 inches
-• Storage: 128GB
-• Color: Space Gray
-• Warranty: 12 months
-• Model Year: 2024
-
-**OCR Confidence:** {random.randint(88, 97)}%
-**Processing Time:** 2.3 seconds
-**Status:** ✅ Ready for inventory update
-"""
-    elif selected_type == "Agroking":
-        result = f"""
-🔍 **OCR PROCESSING COMPLETE**
-
-**Product Detected:** Agroking Premium Fertilizer
-**Product ID:** AGK-2024-{random.randint(100, 999)}
-**Manufacturer:** Agroking
-**Price:** $45.99
-**Quantity:** 5 bags
-**Batch Number:** BN{random.randint(10000, 99999)}
-
-**Extracted Attributes:**
-• Weight: 25kg per bag
-• Type: NPK Fertilizer
-• Composition: 20-10-10
-• Expiry Date: 2025-06-30
-• Organic Certified: Yes
-
-**OCR Confidence:** {random.randint(85, 94)}%
-**Processing Time:** 1.8 seconds
-**Status:** ✅ Ready for inventory update
-"""
-    elif selected_type == "Jaguar":
-        result = f"""
-🔍 **OCR PROCESSING COMPLETE**
-
-**Product Detected:** Jaguar Power Drill
-**Product ID:** JAG-2024-{random.randint(100, 999)}
-**Manufacturer:** Jaguar Inc
-**Price:** $129.99
-**Quantity:** 1 unit
-**Serial Number:** JI{random.randint(10000, 99999)}
-
-**Extracted Attributes:**
-• Power: 850W
-• Voltage: 220V
-• Warranty: 24 months
-• Accessories: 10 drill bits included
-• Chuck Size: 13mm
-
-**OCR Confidence:** {random.randint(90, 98)}%
-**Processing Time:** 2.1 seconds
-**Status:** ✅ Ready for inventory update
-"""
-    else:
-        result = f"""
-🔍 **OCR PROCESSING COMPLETE**
-
-**Product Detected:** Generic Product
-**Product ID:** GEN-{random.randint(1000, 9999)}
-**Manufacturer:** Unknown
-**Price:** ${random.randint(10, 500)}.99
-**Quantity:** {random.randint(1, 10)} units
-
-**Extracted Attributes:**
-• Category: Miscellaneous
-• Condition: New
-• Barcode: {random.randint(100000000000, 999999999999)}
-
-**OCR Confidence:** {random.randint(70, 85)}%
-**Processing Time:** 3.2 seconds
-**Status:** ⚠️ Manual verification recommended
-"""
-    
-    return result
-
-def simulate_salesforce_sync():
-    """Simulate Salesforce synchronization"""
-    # Simulate processing time
-    success_rate = random.random()
-    
-    if success_rate > 0.1:  # 90% success rate
-        sf_id = f"SF-{random.randint(100000, 999999)}"
-        sync_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        
-        result = f"""
-☁️ **SALESFORCE SYNC SUCCESSFUL**
-
-**Sync Details:**
-• **Salesforce ID:** {sf_id}
-• **Sync Time:** {sync_time}
-• **Objects Updated:** Product2, PricebookEntry, Inventory__c
-• **Fields Mapped:** 12
-• **Records Created:** 1
-• **Records Updated:** 3
-
-**Sync Summary:**
-✅ Product information synced
-✅ Pricing data updated
-✅ Inventory levels synchronized
-✅ Supplier information mapped
-✅ Location data updated
-
-**Next Steps:**
-• Product is now available in Salesforce
-• Inventory alerts configured
-• Price book entries created
-• Workflow rules activated
-
-**Sync Status:** Complete
-**Data Integrity:** 100% verified
-"""
-    else:
-        error_types = ["Authentication Error", "Field Mapping Error", "Network Timeout", "Duplicate Record"]
-        error = random.choice(error_types)
-        attempt_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        
-        result = f"""
-❌ **SALESFORCE SYNC FAILED**
-
-**Error Details:**
-• **Error Type:** {error}
-• **Attempt Time:** {attempt_time}
-• **Error Code:** SF-ERR-{random.randint(1000, 9999)}
-
-**Possible Causes:**
-• Network connectivity issues
-• Invalid field mappings
-• Authentication token expired
-• Duplicate record constraints
-
-**Recommended Actions:**
-• Check network connection
-• Verify Salesforce credentials
-• Review field mapping configuration
-• Contact system administrator
-
-**Retry Options:**
-• Automatic retry in 5 minutes
-• Manual retry available
-• Batch sync alternative
-"""
-    
-    return result
-
-def search_customers(search_term):
-    """Search customers"""
-    if not search_term:
-        return get_customers_table()
-    
-    filtered_customers = [
-        customer for customer in customers_data
-        if search_term.lower() in customer["name"].lower()
-        or search_term.lower() in customer["email"].lower()
-        or search_term.lower() in customer["company"].lower()
-        or search_term.lower() in customer["id"].lower()
-    ]
-    
-    df_data = []
-    for customer in filtered_customers:
-        avg_order = customer["total_spent"] / customer["total_orders"] if customer["total_orders"] > 0 else 0
-        df_data.append([
-            customer["id"],
-            customer["name"],
-            customer["email"],
-            customer["phone"],
-            customer["company"],
-            customer["total_orders"],
-            f"${customer['total_spent']:,.2f}",
-            f"${avg_order:.2f}",
-            customer["status"]
-        ])
-    
-    df = pd.DataFrame(df_data, columns=[
-        "Customer ID", "Name", "Email", "Phone", "Company", 
-        "Total Orders", "Total Spent", "Avg Order Value", "Status"
-    ])
-    return df
-
-def get_customers_table():
-    """Get customers as DataFrame"""
-    df_data = []
-    for customer in customers_data:
-        avg_order = customer["total_spent"] / customer["total_orders"] if customer["total_orders"] > 0 else 0
-        df_data.append([
-            customer["id"],
-            customer["name"],
-            customer["email"],
-            customer["phone"],
-            customer["company"],
-            customer["total_orders"],
-            f"${customer['total_spent']:,.2f}",
-            f"${avg_order:.2f}",
-            customer["status"]
-        ])
-    
-    df = pd.DataFrame(df_data, columns=[
-        "Customer ID", "Name", "Email", "Phone", "Company", 
-        "Total Orders", "Total Spent", "Avg Order Value", "Status"
-    ])
-    return df
-
-def refresh_charts():
-    """Refresh all charts"""
-    return create_sales_chart(), create_inventory_chart(), create_customer_chart()
-
-# Create simple interfaces
-with gr.Blocks(title="SETA Inventory System") as demo:
-    gr.Markdown("# 📦 SETA Inventory Management System")
-    
-    with gr.Tabs():
-        with gr.Tab("📊 Dashboard"):
-            gr.Markdown("## Dashboard Overview")
-            dashboard_btn = gr.Button("Refresh Dashboard")
-            dashboard_output = gr.Textbox(label="Dashboard", lines=15, value=show_dashboard())
-            dashboard_btn.click(show_dashboard, outputs=dashboard_output)
-            
-        with gr.Tab("📦 Inventory"):
-            gr.Markdown("## Product Inventory")
-            search_input = gr.Textbox(label="Search Products", placeholder="Enter product name or ID")
-            inventory_output = gr.Dataframe(label="Products", value=show_inventory())
-            search_input.change(search_products, inputs=search_input, outputs=inventory_output)
-            
-        with gr.Tab("🔍 Scanner"):
-            gr.Markdown("## Product Scanner")
-            scan_input = gr.Textbox(label="Scan Product", placeholder="Enter product ID or name")
-            scan_output = gr.Textbox(label="Scan Result", lines=10)
-            scan_btn = gr.Button("Scan")
-            scan_btn.click(scan_product, inputs=scan_input, outputs=scan_output)
-            
-        with gr.Tab("➕ Add Product"):
-            gr.Markdown("## Add New Product")
-            with gr.Row():
-                new_id = gr.Textbox(label="Product ID")
-                new_name = gr.Textbox(label="Product Name")
-            with gr.Row():
-                new_category = gr.Dropdown(["Electronics", "Furniture", "Accessories", "Lighting"], label="Category")
-                new_stock = gr.Number(label="Stock", value=0)
-            with gr.Row():
-                new_min_stock = gr.Number(label="Min Stock", value=5)
-                new_price = gr.Number(label="Price", value=0.0)
-            
-            add_btn = gr.Button("Add Product")
-            add_output = gr.Textbox(label="Result")
-            add_btn.click(
-                add_product,
-                inputs=[new_id, new_name, new_category, new_stock, new_min_stock, new_price],
-                outputs=add_output
-            )
-            
-        with gr.Tab("📝 Update Stock"):
-            gr.Markdown("## Update Stock Levels")
-            product_dropdown = gr.Dropdown(
-                choices=[f"{item['id']} - {item['name']}" for item in inventory_data],
-                label="Select Product"
-            )
-            stock_input = gr.Number(label="New Stock Amount")
-            update_btn = gr.Button("Update Stock")
-            update_output = gr.Textbox(label="Update Result")
-            
-            def update_wrapper(product_selection, new_stock):
-                if product_selection:
-                    product_id = product_selection.split(" - ")[0]
-                    return update_stock_level(product_id, new_stock)
-                return "Please select a product"
-            
-            update_btn.click(
-                update_wrapper,
-                inputs=[product_dropdown, stock_input],
-                outputs=update_output
-            )
-        
-        with gr.Tab("📈 Analytics"):
-            gr.Markdown("## Business Analytics")
-            refresh_btn = gr.Button("Refresh Charts")
-            
-            with gr.Row():
-                sales_chart = gr.Plot(label="Sales Trend")
-                inventory_chart = gr.Plot(label="Inventory Distribution")
-            
-            customer_chart = gr.Plot(label="Customer Status")
-            
-            # Set initial chart values
-            refresh_btn.click(
-                refresh_charts,
-                outputs=[sales_chart, inventory_chart, customer_chart]
-            )
-        
-        with gr.Tab("👥 Customers"):
-            gr.Markdown("## Customer Directory")
-            customer_search_input = gr.Textbox(label="Search Customers", placeholder="Enter customer name, email, company, or ID")
-            customer_output = gr.Dataframe(label="Customers", value=get_customers_table())
-            customer_search_input.change(search_customers, inputs=customer_search_input, outputs=customer_output)
-        
-        with gr.Tab("📷 OCR Scanner"):
-            gr.Markdown("## OCR Scanner")
-            ocr_image_input = gr.Image(label="Upload Product Image")
-            ocr_output = gr.Textbox(label="OCR Results", lines=20)
-            ocr_image_input.change(process_ocr_image, inputs=ocr_image_input, outputs=ocr_output)
-        
-        with gr.Tab("☁️ Salesforce Sync"):
-            gr.Markdown("## Salesforce Sync")
-            salesforce_sync_btn = gr.Button("Sync with Salesforce")
-            salesforce_sync_output = gr.Textbox(label="Salesforce Sync Results", lines=20)
-            salesforce_sync_btn.click(simulate_salesforce_sync, outputs=salesforce_sync_output)
-
-if __name__ == "__main__":
-    print("🚀 Starting SETA Inventory System...")
-    demo.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860
-    )
+from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+from simple_salesforce import Salesforce
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+# CORS for frontend communication
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Serve static files (Next.js build)
+app.mount("/", StaticFiles(directory="static", html=True), name="static")
+
+# Salesforce setup (replace with real creds)
+sf = Salesforce(
+    username='your@email.com',
+    password='yourpassword',
+    security_token='yoursecuritytoken'
+)
+
+@app.get("/api/inventory")
+def get_inventory():
+    result = sf.query("SELECT Id, Name, Quantity__c FROM Inventory__c")
+    return result['records']