app.py

import pandas as pd
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns
import io
import base64

# --- Global Variables to store processed data ---
global_df = None
global_brand_resale = None
global_brand_resale_mean = 0
global_fair_market_value_mean = 0
global_purchase_amount_mean = 0
global_monthly_payment_mean = 0
global_ownership_types = []

# === Truck ID Cleaner ===
def clean_truck_id(val):
    """
    Cleans and standardizes truck IDs by removing "SPOT-" prefix and stripping whitespace.
    Handles NaN values by returning None.
    """
    if pd.isna(val):
        return None
    return str(val).strip().replace("SPOT-", "")

# === Load and Prepare Data ===
def load_and_clean_data():
    """
    Loads data from various Excel files, performs initial cleaning,
    and converts relevant columns to appropriate data types (numeric, datetime).
    Includes error handling for missing files.
    """
    try:
        # Load files
        finance = pd.read_excel("truck-finance.xlsx")
        maintenance = pd.read_excel("maintenancepo-truck.xlsx")
        distance = pd.read_excel("vehicle-distance-traveled.xlsx")
        odometer = pd.read_excel("truck-odometer-data-week.xlsx")
        stub = pd.read_excel("stub-data.xlsx")
        paper = pd.read_excel("truck-paper.xlsx")

        # --- Explicitly convert relevant columns to numeric and datetime ---
        # Coerce errors will turn unparseable values into NaN
        finance['fair_market_value'] = pd.to_numeric(finance['fair_market_value'], errors='coerce')
        finance['purchase_amount'] = pd.to_numeric(finance['purchase_amount'], errors='coerce')
        finance['monthly_payment'] = pd.to_numeric(finance['monthly_payment'], errors='coerce')
        
        maintenance['amount'] = pd.to_numeric(maintenance['amount'], errors='coerce')
        
        distance['date'] = pd.to_datetime(distance['date'], errors='coerce')
        distance['distance'] = pd.to_numeric(distance['distance'], errors='coerce')
        
        odometer['pay_date'] = pd.to_datetime(odometer['pay_date'], errors='coerce')
        odometer['odometer'] = pd.to_numeric(odometer['odometer'], errors='coerce')
        
        paper['truck_price'] = pd.to_numeric(paper['truck_price'], errors='coerce')

        print("Finance columns after loading:", finance.columns)
        print("Maintenance columns after loading:", maintenance.columns)
        print("Stub columns after loading:", stub.columns)
        print("Distance columns after loading:", distance.columns)
        print("Odometer columns after loading:", odometer.columns)

        # --- Clean & Standardize IDs ---
        finance["truck_id"] = finance["unit_id"].apply(clean_truck_id)
        maintenance["truck_id"] = maintenance["unit_id"].apply(clean_truck_id)
        stub["truck_id"] = stub["TRUCK"].apply(clean_truck_id)
        odometer["truck_id"] = odometer["unit_id"].apply(clean_truck_id)
        distance["truck_id"] = distance["unit_id"].apply(clean_truck_id)

        return finance, maintenance, distance, odometer, stub, paper
    except FileNotFoundError as e:
        print(f"Error: One or more input files not found. Please ensure all Excel files are in the same directory as the script. Missing file: {e.filename}")
        raise gr.Error(f"Required file not found: {e.filename}. Please upload all necessary Excel files.")
    except Exception as e:
        print(f"An unexpected error occurred during data loading: {e}")
        raise gr.Error(f"An error occurred during data loading: {e}")

# === Initial Data Processing (called once at app startup) ===
def initial_data_processing():
    """
    Loads, cleans, merges, and prepares all data for the Gradio app.
    Populates global variables used by prediction and plotting functions.
    """
    global global_df, global_brand_resale, global_brand_resale_mean, \
           global_fair_market_value_mean, global_purchase_amount_mean, \
           global_monthly_payment_mean, global_ownership_types

    try:
        finance, maintenance, distance, odometer, stub, paper = load_and_clean_data()

        # --- Maintenance Summary ---
        maintenance_summary = maintenance.groupby("truck_id").agg(
            total_repairs=("amount", "sum"),
            shop_visits=("truck_id", "count")
        ).reset_index()

        # --- Stub Usage ---
        stub_summary = stub.groupby("truck_id").agg(
            usage_records=("truck_id", "count")
        ).reset_index()

        # --- 10-Week Distance Summary ---
        latest = distance['date'].max()
        last10 = distance[distance['date'].notna() & (distance['date'] >= (latest - pd.Timedelta(weeks=10)))]
        distance_summary = last10.groupby("truck_id").agg(
            last_10w_miles=('distance', 'sum')
        ).reset_index()

        # --- Odometer Summary ---
        odometer_cleaned = odometer[odometer['pay_date'].notna() & odometer['odometer'].notna()]
        odo_summary = odometer_cleaned.sort_values(['truck_id', 'pay_date']).groupby("truck_id").agg(
            odo_start=('odometer', 'first'),
            odo_end=('odometer', 'last')
        ).reset_index()
        odo_summary["odo_diff"] = odo_summary["odo_end"] - odo_summary["odo_start"]

        # --- Resale Values (avg per make) ---
        paper['truck_brand'] = paper['truck_brand'].str.upper()
        global_brand_resale = paper.groupby("truck_brand").agg(
            avg_resale_value=('truck_price', 'mean')
        ).reset_index()
        global_brand_resale_mean = global_brand_resale['avg_resale_value'].mean()

        # --- Merge All Sources ---
        df = finance.merge(maintenance_summary, on="truck_id", how="left")
        df = df.merge(stub_summary, on="truck_id", how="left")
        df = df.merge(distance_summary, on="truck_id", how="left")
        df = df.merge(odo_summary[['truck_id', 'odo_diff']], on="truck_id", how="left")

        df['make'] = df['make'].str.upper()
        df = df.merge(global_brand_resale, left_on='make', right_on='truck_brand', how='left')
        df.drop(columns=['truck_brand'], inplace=True)

        # --- Standardize 'ownership_type' ---
        df['ownership_type'] = df['ownership_type'].astype(str).str.strip().str.upper()
        global_ownership_types = df['ownership_type'].unique().tolist()
        # Ensure 'NAN' is handled if it appears due to missing ownership types
        if 'NAN' in global_ownership_types:
            global_ownership_types.remove('NAN')
        global_ownership_types.sort() # Sort for better display in dropdown

        # Calculate means for imputation, handling potential NaN means if column is all NaN
        global_fair_market_value_mean = df['fair_market_value'].mean()
        global_purchase_amount_mean = df['purchase_amount'].mean()
        global_monthly_payment_mean = df['monthly_payment'].mean()

        # --- Handle NaNs for decision-making columns ---
        df["total_repairs"] = df["total_repairs"].fillna(0)
        df["shop_visits"] = df["shop_visits"].fillna(0)
        df["usage_records"] = df["usage_records"].fillna(0)
        df["last_10w_miles"] = df["last_10w_miles"].fillna(0)
        
        df["odo_diff"] = df["odo_diff"].fillna(0).apply(lambda x: 0 if x < 0 else x)
        
        df["avg_resale_value"] = df["avg_resale_value"].fillna(global_brand_resale_mean if not pd.isna(global_brand_resale_mean) else 0)
        df["fair_market_value"] = df["fair_market_value"].fillna(global_fair_market_value_mean if not pd.isna(global_fair_market_value_mean) else 0)
        df["purchase_amount"] = df["purchase_amount"].fillna(global_purchase_amount_mean if not pd.isna(global_purchase_amount_mean) else 0)
        df["monthly_payment"] = df["monthly_payment"].fillna(global_monthly_payment_mean if not pd.isna(global_monthly_payment_mean) else 0)

        # --- Add CPM ---
        # Replace odo_diff = 0 with 1 for CPM calculation to avoid division by zero and get non-zero CPM
        df['odo_diff_for_cpm'] = df['odo_diff'].replace(0, 1)
        df["CPM"] = df["total_repairs"] / df["odo_diff_for_cpm"]
        df["CPM"] = df["CPM"].replace([np.inf, -np.inf], np.nan)
        df["CPM"] = df["CPM"].fillna(0) 

        # --- Apply decision logic to the full dataset for plotting the breakdown ---
        def make_decision_for_df(row):
            # This is the same logic as before, applied to the full DataFrame
            # 1. Scrap:
            if (row['total_repairs'] > 8000 and
                row['last_10w_miles'] < 500 and
                row['odo_diff'] > 70000 and
                row['CPM'] > 0.2 and
                row['purchase_amount'] < 20000):
                return "Scrap"
            # 2. Sell:
            elif (row['total_repairs'] > 5000 and
                  row['last_10w_miles'] < 1000 and
                  row['fair_market_value'] > row['purchase_amount'] and
                  row['odo_diff'] > 50000):
                return "Sell"
            # 3. Lease:
            elif (row['ownership_type'] == 'OPERATING LEASE' and 
                  row['monthly_payment'] > 600 and
                  row['purchase_amount'] < 30000 and
                  row['fair_market_value'] > 28000 and
                  row['odo_diff'] < 40000):
                return "Lease"
            # 4. Keep:
            elif (row['total_repairs'] < 3000 and
                  row['last_10w_miles'] > 2000 and
                  row['fair_market_value'] < row['purchase_amount'] and
                  row['odo_diff'] < 30000):
                return "Keep"
            # 5. Analyze: Default
            else:
                return "Analyze"

        df["Decision"] = df.apply(make_decision_for_df, axis=1)
        
        global_df = df.copy() # Make a copy to avoid SettingWithCopyWarning if modified later
        print("Initial data processing complete. Data loaded for Gradio app.")

    except gr.Error as e:
        print(f"Gradio Error during initial data processing: {e}")
        # If an error occurs, ensure global_df is an empty DataFrame to prevent further errors
        global_df = pd.DataFrame() 
    except Exception as e:
        print(f"Unexpected error during initial data processing: {e}")
        global_df = pd.DataFrame()


# === Decision Prediction Function for Gradio Interface ===
def predict_decision(total_repairs, last_10w_miles, odo_diff, cpm, purchase_amount, fair_market_value, monthly_payment, ownership_type_str, make):
    """
    Predicts the decision for a single truck based on user inputs.
    Uses globally pre-calculated means for missing values if inputs are None.
    """
    # Handle potentially None inputs from Gradio and ensure numeric types
    total_repairs = float(total_repairs) if total_repairs is not None else 0.0
    last_10w_miles = float(last_10w_miles) if last_10w_miles is not None else 0.0
    odo_diff = float(odo_diff) if odo_diff is not None else 0.0
    cpm = float(cpm) if cpm is not None else 0.0

    # Use global means for financial values if user input is None, and ensure they are float
    purchase_amount = float(purchase_amount) if purchase_amount is not None else (global_purchase_amount_mean if not pd.isna(global_purchase_amount_mean) else 0.0)
    fair_market_value = float(fair_market_value) if fair_market_value is not None else (global_fair_market_value_mean if not pd.isna(global_fair_market_value_mean) else 0.0)
    monthly_payment = float(monthly_payment) if monthly_payment is not None else (global_monthly_payment_mean if not pd.isna(global_monthly_payment_mean) else 0.0)
    
    ownership_type_str = ownership_type_str.strip().upper() if ownership_type_str is not None else "UNKNOWN"
    make = make.strip().upper() if make is not None else "UNKNOWN"

    # For avg_resale_value, try to get it from the pre-calculated global_brand_resale, else use global mean
    avg_resale_value = 0.0 # Default if global_brand_resale is not loaded
    if global_brand_resale is not None:
        avg_resale_value_lookup = global_brand_resale.loc[global_brand_resale['truck_brand'] == make, 'avg_resale_value'].values
        if len(avg_resale_value_lookup) > 0:
            avg_resale_value = avg_resale_value_lookup[0]
        else:
            avg_resale_value = global_brand_resale_mean if not pd.isna(global_brand_resale_mean) else 0.0


    # Apply the same logic as make_decision, but directly with the input variables
    # 1. Scrap:
    if (total_repairs > 8000 and
        last_10w_miles < 500 and
        odo_diff > 70000 and
        cpm > 0.2 and
        purchase_amount < 20000):
        return "Scrap"

    # 2. Sell:
    elif (total_repairs > 5000 and
          last_10w_miles < 1000 and
          fair_market_value > purchase_amount and
          odo_diff > 50000):
        return "Sell"

    # 3. Lease:
    elif (ownership_type_str == 'OPERATING LEASE' and 
          monthly_payment > 600 and
          purchase_amount < 30000 and
          fair_market_value > 28000 and
          odo_diff < 40000):
        return "Lease"

    # 4. Keep:
    elif (total_repairs < 3000 and
          last_10w_miles > 2000 and
          fair_market_value < purchase_amount and
          odo_diff < 30000):
        return "Keep"

    # 5. Analyze: Default
    else:
        return "Analyze"

# === Plot Generation Function for Gradio Interface ===
def generate_plots():
    """
    Generates various plots from the processed global_df and returns them as base64 encoded images.
    """
    if global_df is None or global_df.empty:
        # Return a list of None values for the images if data is not loaded
        return [None, None, None, None]

    plot_buffers = [] # Store image bytes here

    # Plot 1: Decision Breakdown
    try:
        plt.figure(figsize=(8, 6))
        # Add hue and set legend=False to resolve FutureWarning
        sns.countplot(data=global_df, x='Decision', hue='Decision', palette='viridis', order=global_df['Decision'].value_counts().index, legend=False)
        plt.title('Decision Breakdown for the Fleet')
        plt.xlabel('Decision')
        plt.ylabel('Number of Trucks')
        plt.grid(axis='y', linestyle='--', alpha=0.7)
        buf = io.BytesIO()
        plt.savefig(buf, format='png')
        plt.close()
        # Encode to base64 and prepend data URI
        plot_buffers.append(base64.b64encode(buf.getvalue()).decode('utf-8'))
    except Exception as e:
        print(f"Error generating Decision Breakdown plot: {e}")
        plot_buffers.append(None) # Append None if plot generation fails

    # Plot 2: Total Repairs by Ownership Type
    try:
        plt.figure(figsize=(12, 7))
        # Filter out NaN/None ownership types if any remain for plotting robustness
        plot_df = global_df[global_df['ownership_type'].notna() & (global_df['ownership_type'] != 'NAN')]
        if not plot_df.empty:
            # Add hue and set legend=False to resolve FutureWarning
            sns.boxplot(data=plot_df, x='ownership_type', y='total_repairs', hue='ownership_type', palette='coolwarm', legend=False)
            plt.title('Total Repairs by Ownership Type')
            plt.xlabel('Ownership Type')
            plt.ylabel('Total Repairs ($)')
            plt.xticks(rotation=45, ha='right')
            plt.grid(axis='y', linestyle='--', alpha=0.7)
            plt.tight_layout()
            buf = io.BytesIO()
            plt.savefig(buf, format='png')
            plt.close()
            # Encode to base64 and prepend data URI
            plot_buffers.append(base64.b64encode(buf.getvalue()).decode('utf-8'))
        else:
            plot_buffers.append(None)
    except Exception as e:
        print(f"Error generating Total Repairs plot: {e}")
        plot_buffers.append(None)

    # Plot 3: Last 10 Weeks Miles Distribution
    try:
        plt.figure(figsize=(10, 6))
        sns.histplot(data=global_df, x='last_10w_miles', bins=30, kde=True, color='skyblue')
        plt.title('Distribution of Last 10 Weeks Miles')
        plt.xlabel('Last 10 Weeks Miles')
        plt.ylabel('Number of Trucks')
        plt.grid(axis='y', linestyle='--', alpha=0.7)
        buf = io.BytesIO()
        plt.savefig(buf, format='png')
        plt.close()
        # Encode to base64 and prepend data URI
        plot_buffers.append(base64.b64encode(buf.getvalue()).decode('utf-8'))
    except Exception as e:
        print(f"Error generating Miles Distribution plot: {e}")
        plot_buffers.append(None)

    # Plot 4: Fair Market Value vs. Purchase Amount
    try:
        plt.figure(figsize=(10, 7))
        # Ensure columns are numeric and handle potential NaNs for plotting
        plot_df = global_df.dropna(subset=['purchase_amount', 'fair_market_value', 'Decision'])
        if not plot_df.empty:
            sns.scatterplot(data=plot_df, x='purchase_amount', y='fair_market_value', hue='Decision', palette='deep', alpha=0.7)
            plt.title('Fair Market Value vs. Purchase Amount by Decision')
            plt.xlabel('Purchase Amount ($)')
            plt.ylabel('Fair Market Value ($)')
            plt.grid(linestyle='--', alpha=0.7)
            plt.tight_layout()
            buf = io.BytesIO()
            plt.savefig(buf, format='png')
            plt.close()
            # Encode to base64 and prepend data URI
            plot_buffers.append(base64.b64encode(buf.getvalue()).decode('utf-8'))
        else:
            plot_buffers.append(None)
    except Exception as e:
        print(f"Error generating FMV vs Purchase plot: {e}")
        plot_buffers.append(None)

    return plot_buffers

# --- Initial Data Loading and Processing Call ---
initial_data_processing()

# --- Gradio Interface Definition ---

# Define inputs for the Decision Predictor tab
# Use the dynamically populated global_ownership_types for the dropdown choices
decision_inputs = [
    gr.Number(label="Total Repairs ($)", value=0.0),
    gr.Number(label="Last 10 Weeks Miles", value=0.0),
    gr.Number(label="Odometer Difference (odo_diff)", value=0.0),
    gr.Number(label="Cost Per Mile (CPM)", value=0.0),
    gr.Number(label="Purchase Amount ($)", value=0.0),
    gr.Number(label="Fair Market Value ($)", value=0.0),
    gr.Number(label="Monthly Payment ($)", value=0.0),
    gr.Dropdown(label="Ownership Type", choices=global_ownership_types, value=global_ownership_types[0] if global_ownership_types else "OWNER OPERATOR OWNED"),
    gr.Textbox(label="Make (e.g., FORD)", value="FORD")
]

# Create the Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# Truck Evaluation Application")
    gr.Markdown("Use this app to predict truck decisions and visualize fleet data.")

    with gr.Tab("Decision Predictor"):
        gr.Markdown("## Predict Truck Decision")
        gr.Markdown("Enter the details for a single truck to get a decision.")
        
        with gr.Row():
            for input_comp in decision_inputs:
                input_comp.render()
        
        predict_button = gr.Button("Get Decision")
        decision_output = gr.Textbox(label="Decision", interactive=False)
        
        predict_button.click(
            fn=predict_decision,
            inputs=decision_inputs,
            outputs=decision_output
        )

    with gr.Tab("Data Visualizations"):
        gr.Markdown("## Fleet Data Visualizations")
        gr.Markdown("Explore insights from your truck data.")
        
        plot_button = gr.Button("Generate Plots")
        
        # Output components for plots
        # These are just placeholders; the generate_plots function will return the actual image bytes
        plot_outputs_components = [
            gr.Image(label="Decision Breakdown", interactive=False, visible=True),
            gr.Image(label="Total Repairs by Ownership Type", interactive=False, visible=True),
            gr.Image(label="Last 10 Weeks Miles Distribution", interactive=False, visible=True),
            gr.Image(label="Fair Market Value vs. Purchase Amount", interactive=False, visible=True)
        ]
        
        plot_button.click(
            fn=generate_plots,
            inputs=[],
            outputs=plot_outputs_components
        )

# Launch the Gradio app
if __name__ == "__main__":
    demo.launch()