Created new App.py

app.py

@@ -0,0 +1,120 @@
+import gradio as gr
+import pandas as pd
+import joblib
+import plotly.express as px
+import plotly.graph_objects as go
+
+# --- 1. DATA & MODEL LOADING ---
+# Load your model and feature list
+model = joblib.load("sales_model.joblib")
+feature_names = joblib.load("feature_names.joblib")
+
+# Load your CSV for EDA charts
+df = pd.read_csv("sales_data.csv")
+df['date'] = pd.to_datetime(df['date'])
+
+# --- 2. PREDICTION LOGIC ---
+def predict_sales(u_date, u_brand, u_region, u_price, u_promo, u_stock):
+    target_date = pd.to_datetime(u_date)
+    
+    # Initialize input row with zeros matching training features
+    input_row = pd.DataFrame(0, index=[0], columns=feature_names)
+    
+    # Fill Time Features
+    input_row['month'] = target_date.month
+    input_row['day_of_week'] = target_date.weekday()
+    input_row['is_weekend'] = 1 if target_date.weekday() >= 5 else 0
+    
+    # Fill Numerical Features
+    input_row['price_unit'] = u_price
+    input_row['promotion'] = 1 if u_promo else 0
+    input_row['stock_ava'] = u_stock
+    
+    # Fill Categorical Features (One-Hot Encoding match)
+    brand_col = f"brand_{u_brand}"
+    region_col = f"region_{u_region}"
+    
+    if brand_col in input_row.columns: input_row[brand_col] = 1
+    if region_col in input_row.columns: input_row[region_col] = 1
+            
+    # Predict
+    prediction = model.predict(input_row)[0]
+    # Ensure no negative sales
+    prediction = max(0, int(prediction))
+    revenue = prediction * u_price
+    
+    return f"{prediction} Units", f"${revenue:,.2f}"
+
+# --- 3. EDA CHART FUNCTIONS ---
+def plot_weekly_trends():
+    df['weekday'] = df['date'].dt.day_name()
+    order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
+    weekly_avg = df.groupby('weekday')['units_sold'].mean().reindex(order).reset_index()
+    fig = px.bar(weekly_avg, x='weekday', y='units_sold', color='units_sold',
+                 title="Average Units Sold by Day of Week", color_continuous_scale='Blues')
+    return fig
+
+def plot_price_elasticity():
+    fig = px.scatter(df, x="price_unit", y="units_sold", color="promotion",
+                     trendline="ols", title="Price vs. Units Sold (Elasticity)")
+    return fig
+
+def plot_regional_share():
+    fig = px.sunburst(df, path=['region', 'category'], values='units_sold',
+                      title="Regional Sales Distribution by Category")
+    return fig
+
+def plot_correlation():
+    corr = df[['price_unit', 'stock_ava', 'units_sold', 'promotion']].corr()
+    fig = px.imshow(corr, text_auto=True, title="Feature Correlation Heatmap",
+                    color_continuous_scale='RdBu_r')
+    return fig
+
+# --- 4. GRADIO UI LAYOUT ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🥛 Smart Sales & Revenue Intelligence")
+    gr.Markdown("Predict future performance and explore historical data patterns.")
+
+    with gr.Tabs():
+        # TAB 1: PREDICTOR
+        with gr.TabItem("🔮 Sales Forecast"):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Input Parameters")
+                    u_date = gr.DateTime(label="Select Target Date")
+                    u_brand = gr.Dropdown(sorted(df['brand'].unique().tolist()), label="Brand")
+                    u_region = gr.Dropdown(sorted(df['region'].unique().tolist()), label="Region")
+                    u_price = gr.Number(label="Unit Price ($)", value=2.50)
+                    u_promo = gr.Checkbox(label="Is Promotion Active?")
+                    u_stock = gr.Slider(0, 500, label="Stock Availability", value=150)
+                    predict_btn = gr.Button("Generate Prediction", variant="primary")
+                
+                with gr.Column():
+                    gr.Markdown("### Model Output")
+                    out_sales = gr.Textbox(label="Predicted Sales Volume")
+                    out_rev = gr.Textbox(label="Estimated Gross Revenue")
+                    
+                    gr.Markdown("---")
+                    gr.Info("Note: Predictions are based on historical XGBoost training patterns.")
+
+            predict_btn.click(
+                predict_sales, 
+                inputs=[u_date, u_brand, u_region, u_price, u_promo, u_stock], 
+                outputs=[out_sales, out_rev]
+            )
+
+        # TAB 2: DATA EXPLORATION
+        with gr.TabItem("📊 EDA Dashboard"):
+            with gr.Row():
+                gr.Plot(plot_weekly_trends())
+                gr.Plot(plot_correlation())
+            with gr.Row():
+                gr.Plot(plot_price_elasticity())
+                gr.Plot(plot_regional_share())
+            
+            with gr.Accordion("View Raw Data Snippet", open=False):
+                gr.DataFrame(df.head(10))
+
+# --- 5. LAUNCH ---
+if __name__ == "__main__":
+    demo.launch()