improve analytics
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40ddea4d42
commit
9d744e7948
19
analytics.py
19
analytics.py
@ -179,24 +179,20 @@ def dashboard():
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WHERE timestamp >= ?
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GROUP BY ip_address
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ORDER BY count DESC
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LIMIT 20
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LIMIT 1000
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''', (start.isoformat(),))
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ip_rows = cursor.fetchall()
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# Initialize GeoIP2 reader once for efficiency
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reader = geoip2.database.Reader('GeoLite2-City.mmdb')
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ip_data = []
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location_data = []
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for ip, count in ip_rows:
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country, city = lookup_location(ip, reader)
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ip_data.append(dict(ip=ip, count=count, country=country, city=city))
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location_data.append(dict(count=count, country=country, city=city))
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reader.close()
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# Aggregate by city (ignoring entries without a city)
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city_counts = {}
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for entry in ip_data:
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if entry['city']:
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city_counts[entry['city']] = city_counts.get(entry['city'], 0) + entry['count']
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city_data = [dict(city=city, count=count) for city, count in city_counts.items()]
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# Sort by count in descending order and take the top 20
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location_data.sort(key=lambda x: x['count'], reverse=True)
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location_data = location_data[:20]
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# Summary stats using separate SQL queries
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cursor.execute('SELECT COUNT(*) FROM file_access_log WHERE timestamp >= ?', (start.isoformat(),))
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@ -219,8 +215,7 @@ def dashboard():
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top_files_data=top_files_data,
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user_agent_data=user_agent_data,
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referrer_data=referrer_data,
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ip_data=ip_data,
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city_data=city_data,
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location_data=location_data,
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total_accesses=total_accesses,
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unique_files=unique_files,
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unique_ips=unique_ips)
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@ -41,7 +41,7 @@
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<div class="col-md-4">
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<div class="card text-white bg-info">
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<div class="card-body">
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<h5 class="card-title">Total Accesses</h5>
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<h5 class="card-title">Alle Downloads</h5>
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<p class="card-text">{{ total_accesses }}</p>
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</div>
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</div>
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@ -49,7 +49,7 @@
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<div class="col-md-4">
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<div class="card text-white bg-success">
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<div class="card-body">
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<h5 class="card-title">Unique Files</h5>
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<h5 class="card-title">eindeutige Dateien</h5>
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<p class="card-text">{{ unique_files }}</p>
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</div>
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</div>
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@ -57,7 +57,7 @@
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<div class="col-md-4">
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<div class="card text-white bg-warning">
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<div class="card-body">
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<h5 class="card-title">Unique IPs</h5>
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<h5 class="card-title">eindeutige Nutzer</h5>
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<p class="card-text">{{ unique_ips }}</p>
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</div>
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</div>
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@ -70,7 +70,7 @@
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<div class="col-md-6 mb-4">
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<div class="card">
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<div class="card-body">
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<h5 class="card-title">Access Trend</h5>
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<h5 class="card-title">Downloads</h5>
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<canvas id="accessTrendChart"></canvas>
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</div>
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</div>
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@ -79,7 +79,7 @@
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<div class="col-md-6 mb-4">
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<div class="card">
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<div class="card-body">
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<h5 class="card-title">Top Files Accessed</h5>
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<h5 class="card-title">Häufig geladene Dateien</h5>
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<canvas id="topFilesChart"></canvas>
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</div>
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</div>
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@ -88,7 +88,7 @@
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<div class="col-md-6 mb-4">
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<div class="card">
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<div class="card-body">
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<h5 class="card-title">User Agent Distribution</h5>
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<h5 class="card-title">Verwendete Endgeräte</h5>
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<canvas id="userAgentChart"></canvas>
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</div>
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</div>
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@ -97,7 +97,7 @@
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<div class="col-md-6 mb-4">
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<div class="card">
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<div class="card-body">
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<h5 class="card-title">Referrer Distribution</h5>
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<h5 class="card-title">Verteilung auf Ordner</h5>
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<canvas id="referrerChart"></canvas>
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</div>
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</div>
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@ -107,58 +107,27 @@
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<!-- New Section: IP Address Access Data -->
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<div class="card">
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<div class="card-header">
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IP Address Access Distribution
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Verteilung der Zugriffe
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</div>
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<div class="card-body">
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<table class="table table-bordered">
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<thead>
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<tr>
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<th>IP Address</th>
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<th>Access Count</th>
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<th>City</th>
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<th>Country</th>
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<th>Anzahl Downloads</th>
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<th>Stadt</th>
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<th>Land</th>
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</tr>
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</thead>
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<tbody>
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{% for ip in ip_data %}
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{% for loc in location_data %}
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<tr>
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<td>{{ ip.ip }}</td>
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<td>{{ ip.count }}</td>
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<td>{{ ip.city }}</td>
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<td>{{ ip.country }}</td>
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<td>{{ loc.count }}</td>
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<td>{{ loc.city }}</td>
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<td>{{ loc.country }}</td>
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</tr>
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{% else %}
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<tr>
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<td colspan="4">No IP access data available for the selected timeframe.</td>
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</tr>
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{% endfor %}
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</tbody>
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</table>
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</div>
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</div>
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<!-- New Section: Aggregated City Access Data -->
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<div class="card">
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<div class="card-header">
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City Access Distribution
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</div>
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<div class="card-body">
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<table class="table table-bordered">
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<thead>
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<tr>
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<th>City</th>
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<th>Access Count</th>
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</tr>
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</thead>
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<tbody>
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{% for city in city_data %}
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<tr>
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<td>{{ city.city }}</td>
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<td>{{ city.count }}</td>
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</tr>
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{% else %}
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<tr>
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<td colspan="2">No city access data available for the selected timeframe.</td>
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<td colspan="4">No access data available for the selected timeframe.</td>
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</tr>
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{% endfor %}
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</tbody>
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@ -169,7 +138,7 @@
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<!-- Detailed Table of Top File Accesses -->
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<div class="card mb-4">
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<div class="card-header">
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Detailed File Access Data
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Detailierte Dateizugriffe (Top 20)
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</div>
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<div class="card-body">
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<table class="table table-striped">
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@ -214,7 +183,7 @@
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data: {
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labels: dailyAccessData.map(item => item.date),
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datasets: [{
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label: 'Access Count',
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label: 'Download Count',
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data: dailyAccessData.map(item => item.count),
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borderWidth: 2,
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fill: true
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@ -224,8 +193,8 @@
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responsive: true,
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plugins: { legend: { position: 'top' } },
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scales: {
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x: { title: { display: true, text: 'Date' } },
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y: { title: { display: true, text: 'Access Count' } }
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x: { title: { display: true, text: 'Datum' } },
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y: { title: { display: true, text: 'Download Count' } }
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}
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}
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});
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@ -237,7 +206,7 @@
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data: {
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labels: topFilesData.map(item => item.full_path),
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datasets: [{
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label: 'Access Count',
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label: 'Download Count',
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data: topFilesData.map(item => item.access_count),
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borderWidth: 1
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}]
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@ -247,8 +216,8 @@
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responsive: true,
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plugins: { legend: { display: false } },
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scales: {
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x: { title: { display: true, text: 'Access Count' } },
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y: { title: { display: true, text: 'File Path' } }
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x: { title: { display: true, text: 'Download Count' } },
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y: { title: { display: true, text: '' } }
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}
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}
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});
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@ -21,7 +21,7 @@
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</header>
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<div class="container">
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<div id="content">Bitte den Link aus der Telegram-Gruppe erneut anklicken.</div>
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<div id="content">Du hast keine gültigen Links im Speicher.<br>Bitte den Link aus der Telegram-Gruppe erneut anklicken.</div>
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</div>
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</body>
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</html>
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