Delete app (1).py

app (1).py

@@ -1,114 +0,0 @@
-
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from datetime import datetime, timedelta
-import random
-import gradio as gr
-from sklearn.linear_model import LinearRegression
-import tempfile
-
-def generate_synthetic_data(n_days=90):
-    dates = pd.date_range(end=datetime.today(), periods=n_days)
-    data = []
-    store_names = [f"Store_{i}" for i in range(1, 21)]
-    categories = ['Fashion', 'Electronics', 'Food', 'Entertainment', 'Home Decor']
-    zones = ['Ground Floor', 'First Floor', 'Second Floor']
-
-    for date in dates:
-        for store in store_names:
-            visitors = np.random.poisson(lam=150)
-            sales = np.random.normal(loc=visitors * 10, scale=visitors * 2)
-            dwell_time = np.random.normal(loc=30, scale=5)
-            category = random.choice(categories)
-            zone = random.choice(zones)
-            data.append({
-                'date': date,
-                'store': store,
-                'category': category,
-                'zone': zone,
-                'visitors': max(0, visitors),
-                'sales': max(0, sales),
-                'dwell_time': max(5, dwell_time)
-            })
-    return pd.DataFrame(data)
-
-def forecast_sales(df, days_ahead=7):
-    daily_sales = df.groupby('date')['sales'].sum().reset_index()
-    daily_sales['day_num'] = (daily_sales['date'] - daily_sales['date'].min()).dt.days
-    X = daily_sales[['day_num']]
-    y = daily_sales['sales']
-    model = LinearRegression().fit(X, y)
-    last_day = daily_sales['day_num'].max()
-    future_days = np.arange(last_day + 1, last_day + 1 + days_ahead).reshape(-1, 1)
-    future_dates = [daily_sales['date'].max() + timedelta(days=i) for i in range(1, days_ahead + 1)]
-    predictions = model.predict(future_days)
-    forecast_df = pd.DataFrame({'date': future_dates, 'sales': predictions})
-    return forecast_df
-
-def generate_chart(chart_type):
-    df = generate_synthetic_data()
-    forecast_df = forecast_sales(df)
-    
-    daily_visits = df.groupby('date')['visitors'].sum().reset_index()
-    category_sales = df.groupby('category')['sales'].sum().reset_index()
-    zone_traffic = df.groupby('zone')['visitors'].sum().reset_index()
-    top_stores = df.groupby('store')['sales'].sum().nlargest(10).reset_index()
-    dwell_time = df.groupby('category')['dwell_time'].mean().reset_index()
-
-    fig, ax = plt.subplots(figsize=(8, 5))
-    plt.tight_layout()
-
-    if chart_type == "Daily Footfall":
-        ax.plot(daily_visits['date'], daily_visits['visitors'], color='blue')
-        ax.set_title('Daily Footfall')
-        ax.set_xlabel('Date')
-        ax.set_ylabel('Visitors')
-    elif chart_type == "Sales by Category":
-        ax.bar(category_sales['category'], category_sales['sales'], color='green')
-        ax.set_title('Sales by Category')
-        ax.set_ylabel('Sales')
-    elif chart_type == "Footfall by Zone":
-        ax.pie(zone_traffic['visitors'], labels=zone_traffic['zone'], autopct='%1.1f%%')
-        ax.set_title('Footfall by Zone')
-    elif chart_type == "Top 10 Stores by Sales":
-        ax.bar(top_stores['store'], top_stores['sales'], color='purple')
-        ax.set_title('Top 10 Stores by Sales')
-        ax.set_ylabel('Sales')
-        plt.xticks(rotation=45)
-    elif chart_type == "Avg Dwell Time by Category":
-        ax.bar(dwell_time['category'], dwell_time['dwell_time'], color='orange')
-        ax.set_title('Avg Dwell Time by Category')
-        ax.set_ylabel('Dwell Time (mins)')
-    elif chart_type == "Forecasted Sales (Next 7 Days)":
-        ax.plot(forecast_df['date'], forecast_df['sales'], color='red')
-        ax.set_title('Forecasted Sales (Next 7 Days)')
-        ax.set_xlabel('Date')
-        ax.set_ylabel('Predicted Sales')
-    else:
-        return None
-
-    tmp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    plt.savefig(tmp_file.name, bbox_inches='tight')
-    plt.close(fig)
-    return tmp_file.name
-
-with gr.Blocks(title="📊 Mall Dashboard with Matplotlib") as demo:
-    gr.Markdown("## 🏬 Mall Analytics Dashboard (Matplotlib Version)")
-    chart_dropdown = gr.Dropdown(
-        choices=[
-            "Daily Footfall",
-            "Sales by Category",
-            "Footfall by Zone",
-            "Top 10 Stores by Sales",
-            "Avg Dwell Time by Category",
-            "Forecasted Sales (Next 7 Days)"
-        ],
-        value="Daily Footfall",
-        label="Choose a Chart"
-    )
-    generate_button = gr.Button("Generate Chart")
-    image_output = gr.Image(label="Chart Output")
-    generate_button.click(fn=generate_chart, inputs=chart_dropdown, outputs=image_output)
-
-demo.launch()