webui/app.py at main · PuntoMaximo/webui · GitHub

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from flask import Flask, render_template, jsonify, request
import csv
from datetime import datetime
import numpy as np

app = Flask(__name__)

DATA_PATH = './data.csv'
LABELS_PATH = './coco_80_labels_list.txt'

def get_labels():
    with open(LABELS_PATH, 'r') as f:
        return [line.strip() for line in f if line.strip()]

def parse_date(date_str):
    return datetime.strptime(date_str, '%d/%m/%Y %H:%M:%S')

def date_in_range(dt_obj, start_date, end_date):
    if not start_date or not end_date:
        return True
    start = datetime.strptime(start_date, '%Y-%m-%d').date()
    end = datetime.strptime(end_date, '%Y-%m-%d').date()
    return start <= dt_obj.date() <= end

@app.route('/')
def index():
    labels = get_labels()
    lang = request.args.get('lang', 'es')
    return render_template('index.html', labels=labels, lang=lang)

@app.route('/data')
def get_data():
    selected_label = request.args.get('label')
    start_date = request.args.get('start_date')
    end_date = request.args.get('end_date')
    lang = request.args.get('lang', 'es')
    labels = get_labels()
    timeline = process_timeline_data(selected_label, start_date, end_date)
    histogram = process_histogram_data(selected_label, start_date, end_date)
    classification = process_classification_data(labels, start_date, end_date)
    return jsonify({
        'timeline': timeline,
        'histogram': histogram,
        'classification': classification,
        'lang': lang,
        'labels': labels
    })

def process_timeline_data(selected_label, start_date, end_date):
    data = {'timestamps': [], 'objects_detected': []}
    try:
        with open(DATA_PATH, 'r') as file:
            reader = csv.DictReader(file)
            for row in reader:
                if selected_label not in row:
                    continue
                dt_obj = parse_date(row['Timestamp'])
                if not date_in_range(dt_obj, start_date, end_date):
                    continue
                data['timestamps'].append(row['Timestamp'])
                data['objects_detected'].append(int(row[selected_label]))
    except Exception as e:
        app.logger.error(f"Timeline error: {str(e)}")
    return data

def process_histogram_data(selected_label, start_date, end_date):
    hourly_counts = [0] * 24
    try:
        with open(DATA_PATH, 'r') as file:
            reader = csv.DictReader(file)
            for row in reader:
                if selected_label not in row:
                    continue
                dt_obj = parse_date(row['Timestamp'])
                if not date_in_range(dt_obj, start_date, end_date):
                    continue
                hour = dt_obj.hour
                hourly_counts[hour] += int(row[selected_label])
        avg = np.mean(hourly_counts) if any(hourly_counts) else 0
    except Exception as e:
        app.logger.error(f"Histogram error: {str(e)}")
        avg = 0
    return {
        'hours': list(range(24)),
        'counts': hourly_counts,
        'average': [avg] * 24
    }

def process_classification_data(labels, start_date, end_date):
    counts = {label: 0 for label in labels}
    try:
        with open(DATA_PATH, 'r') as file:
            reader = csv.DictReader(file)
            for row in reader:
                dt_obj = parse_date(row['Timestamp'])
                if not date_in_range(dt_obj, start_date, end_date):
                    continue
                for label in labels:
                    if label in row and row[label].isdigit():
                        counts[label] += int(row[label])
    except Exception as e:
        app.logger.error(f"Classification error: {str(e)}")
    return counts

if __name__ == '__main__':
    app.run(debug=True)