backend.py

import pandas as pd
import plotly.express as px
from datetime import datetime
import os
import time
from PIL import Image

# ================= LOAD FILE =================
def load_file(file_path):
    if file_path.endswith(".csv"):
        return pd.read_csv(file_path)
    else:
        return pd.read_excel(file_path)

# ================= GET COLUMNS =================
def get_columns(file_path):
    try:
        df = load_file(file_path)
        return list(df.columns)
    except:
        return []

# ================= APPLY FILTERS =================
def apply_filters(df, filters):
    if not filters:
        return df

    try:
        for f in filters:
            col = f["column"]
            ftype = f["type"]
            val = f["value"]

            if col not in df.columns:
                continue

            # equals filter
            if ftype == "equals":
                df = df[df[col].isin(val)]

            # contains filter
            elif ftype == "contains":
                df = df[df[col].astype(str).str.contains(val[0], case=False, na=False)]

            # range filter
            elif ftype == "range":
                try:
                    min_val, max_val = val
                    df[col] = pd.to_numeric(df[col], errors='coerce')
                    df = df[(df[col] >= min_val) & (df[col] <= max_val)]
                except:
                    continue

        return df

    except:
        return df

# ================= ANALYSIS =================
def sales_insights(df, chart_type, x_col, y_cols):

    if not x_col:
        raise ValueError("Select X-axis")
    if not y_cols:
        raise ValueError("Select Y-axis")

    # Convert numeric columns
    for col in y_cols:
        df[col] = pd.to_numeric(df[col], errors='coerce')

    df = df.dropna(subset=y_cols)

    # Aggregate data
    summary = df.groupby(x_col)[y_cols].sum().reset_index()

    # Create charts
    if chart_type == "Bar Chart":
        fig = px.bar(summary, x=x_col, y=y_cols, barmode="group")

    elif chart_type == "Line Chart":
        fig = px.line(summary, x=x_col, y=y_cols, markers=True)

    elif chart_type == "Pie Chart":
        fig = px.pie(summary, names=x_col, values=y_cols[0])

    elif chart_type == "Box Plot":
        fig = px.box(df, x=x_col, y=y_cols[0], points="all")

    else:
        raise ValueError("Invalid chart type")

    fig.update_layout(template="plotly_white", height=600)

    # Save output files
    chart_path = "output_chart.png"
    data_path = "output_data.xlsx"

    try:
        fig.write_image(chart_path)
    except:
        Image.new('RGB', (1100, 650), color='#eeeeee').save(chart_path)

    summary.to_excel(data_path, index=False)

    return fig, chart_path, data_path


# ================= FEEDBACK =================
def save_feedback(name, comment, stars):
    file = "feedback.xlsx"

    try:
        stars = max(1, min(5, int(stars)))
    except:
        stars = 3

    entry = {
        "Time": datetime.now(),
        "Name": name or "Anonymous",
        "Comment": comment or "",
        "Stars": stars
    }

    for _ in range(3):
        try:
            if os.path.exists(file):
                df = pd.read_excel(file)
            else:
                df = pd.DataFrame(columns=entry.keys())

            df = pd.concat([df, pd.DataFrame([entry])], ignore_index=True)
            df.to_excel(file, index=False)
            return "✅ Saved"
        except:
            time.sleep(0.3)

    return "❌ Error"