app.py

import pandas as pd
from itertools import permutations
from flask import Flask, jsonify, request
from flask_cors import CORS

app = Flask(__name__)
CORS(app)

class Box:
    def __init__(self, length, width, height, quantity, box_type):
        self.length = length
        self.width = width
        self.height = height
        self.quantity = quantity if quantity > 0 else 1  # Ensure at least 1 box if quantity is not specified
        self.type = box_type

    def rotated_dimensions(self):
        # Return rotations that maximize base area and minimize height
        possible_rotations = [
            (self.length, self.width, self.height),
            (self.length, self.height, self.width),
            (self.width, self.length, self.height),
            (self.width, self.height, self.length),
            (self.height, self.length, self.width),
            (self.height, self.width, self.length)
        ]
        # Sort by volume and base area to prioritize the best fit
        return sorted(possible_rotations, key=lambda x: (x[0] * x[1], x[2]), reverse=True)

    def volume(self):
        return self.length * self.width * self.height

class Truck:
    def __init__(self, length, width, height):
        self.length = length
        self.width = width
        self.height = height
        self.volume = length * width * height
        self.placed_boxes = []
        self.available_space = [(0, 0, 0, length, width, height)]  # (x, y, z, l, w, h)

    def add_box(self, box):
        best_fit = None
        best_fit_space_index = -1

        for rotation in box.rotated_dimensions():
            for i, space in enumerate(self.available_space):
                x, y, z, l, w, h = space

                # Check if the box can fit in the current space
                if rotation[0] <= l and rotation[1] <= w and rotation[2] <= h:
                    if not best_fit or (rotation[0] * rotation[1] > best_fit[0] * best_fit[1]):
                        best_fit = rotation
                        best_fit_space_index = i

        if best_fit:
            x, y, z, l, w, h = self.available_space[best_fit_space_index]
            box_position = (x, y, z)

            # Place the box in the truck
            self.placed_boxes.append((best_fit, box_position))

            # Update available space after placing the box
            self.available_space.pop(best_fit_space_index)
            self.update_space(best_fit, box_position, l, w, h)
            return box_position
        else:
            return None

    def update_space(self, box, position, l, w, h):
        x, y, z = position
        bl, bw, bh = box

        # Create new spaces based on the placement of the new box
        new_spaces = [
            (x + bl, y, z, l - bl, w, h),  # Space to the right
            (x, y + bw, z, bl, w - bw, h),  # Space in front
            (x, y, z + bh, bl, bw, h - bh),  # Space above
        ]

        # Filter out invalid spaces
        new_spaces = [space for space in new_spaces if space[3] > 0 and space[4] > 0 and space[5] > 0]

        # Add new valid spaces to the available_space list
        self.available_space.extend(new_spaces)

        # Sort available spaces to prioritize lower and more central spaces for stacking
        self.available_space.sort(key=lambda space: (space[2], space[1], space[0]))

def pack_boxes(truck, boxes):
    packed_positions = []

    # Sort all boxes by volume (largest first)
    boxes.sort(key=lambda b: b.volume(), reverse=True)

    # Pack all boxes, ensuring practical stacking
    for box in boxes:
        for _ in range(box.quantity):
            position = truck.add_box(box)
            if position is None:
                break
            packed_positions.append((box, position))

    return packed_positions

@app.route('/upload', methods=['POST'])
def upload_file():
    file = request.files['file']
    if not file:
        return jsonify({"error": "No file uploaded"}), 400
    
    ext = file.filename.split('.')[-1].lower()
    if ext == 'csv':
        data = pd.read_csv(file)
    elif ext in ['xls', 'xlsx']:
        data = pd.read_excel(file)
    else:
        return jsonify({"error": "Unsupported file format"}), 400
    
    # Convert dimensions from CM to inches
    data['PieceLength'] = data['PieceLength'] / 2.54
    data['PieceBreadth'] = data['PieceBreadth'] / 2.54
    data['PieceHeight'] = data['PieceHeight'] / 2.54

    # Create Box objects with quantity considered
    boxes = [
        Box(row['PieceLength'], row['PieceBreadth'], row['PieceHeight'], row.get('Quantity', 1), f"{row['PieceNo']}-{row['Priority']}")
        for _, row in data.iterrows()
    ]

    # Convert truck dimensions from feet to inches
    truck_length = float(request.form['length']) * 12  # Convert to inches
    truck_width = float(request.form['width']) * 12    # Convert to inches
    truck_height = float(request.form['height']) * 12  # Convert to inches

    truck = Truck(truck_length, truck_width, truck_height)

    # Pack the boxes into the truck
    packed_positions = pack_boxes(truck, boxes)
    
    box_data = [
        {
            'length': box.length,
            'width': box.width,
            'height': box.height,
            'type': box.type,
            'quantity': box.quantity,
            'position': {'x': pos[0], 'y': pos[1], 'z': pos[2]}
        }
        for box, pos in packed_positions
    ]
    print(f"quantity {[box_data[i]['quantity'] for i in range(len(box_data))]}")

    return jsonify(box_data)

if __name__ == '__main__':
    app.run(debug=True , host = "0.0.0.0" , port=8888)