def search(self,
words: str,
disk_loader: DiskCacheLoader,
limit: int = -1,
is_url: bool = False) -> SearchResult:
if is_url:
return SearchResult([], [], 0)
remote_level = self._get_search_remote()
res = SearchResult([], [], 0)
apps_found = flatpak.search(flatpak.get_version(), words, remote_level)
if apps_found:
already_read = set()
installed_apps = self.read_installed(
disk_loader=disk_loader, internet_available=True).installed
if installed_apps:
for app_found in apps_found:
for installed_app in installed_apps:
if app_found['id'] == installed_app.id:
res.installed.append(installed_app)
already_read.add(app_found['id'])
if len(apps_found) > len(already_read):
for app_found in apps_found:
if app_found['id'] not in already_read:
res.new.append(
self._map_to_model(app_found, False, disk_loader))
res.total = len(res.installed) + len(res.new)
return res
def search(self,
words: str,
disk_loader: DiskCacheLoader,
limit: int = -1) -> SearchResult:
res = SearchResult([], [], 0)
apps_found = flatpak.search(flatpak.get_version(), words)
if apps_found:
already_read = set()
installed_apps = self.read_installed(
disk_loader=disk_loader).installed
if installed_apps:
for app_found in apps_found:
for installed_app in installed_apps:
if app_found['id'] == installed_app.id:
res.installed.append(installed_app)
already_read.add(app_found['id'])
if len(apps_found) > len(already_read):
for app_found in apps_found:
if app_found['id'] not in already_read:
res.new.append(
self._map_to_model(app_found, False, disk_loader))
res.total = len(res.installed) + len(res.new)
return res
def _fill_suggestion(self, appid: str, priority: SuggestionPriority, flatpak_version: Version, remote: str,
output: List[PackageSuggestion]):
app_json = flatpak.search(flatpak_version, appid, remote, app_id=True)
if app_json:
model = PackageSuggestion(self._map_to_model(app_json[0], False, None)[0], priority)
self.suggestions_cache.add(appid, model)
output.append(model)
else:
self.logger.warning(f"Could not find Flatpak suggestions '{appid}'")
def list_suggestions(self, limit: int,
filter_installed: bool) -> List[PackageSuggestion]:
cli_version = flatpak.get_version()
res = []
self.logger.info(
"Downloading the suggestions file {}".format(SUGGESTIONS_FILE))
file = self.http_client.get(SUGGESTIONS_FILE)
if not file or not file.text:
self.logger.warning(
"No suggestion found in {}".format(SUGGESTIONS_FILE))
return res
else:
self.logger.info("Mapping suggestions")
remote_level = self._get_search_remote()
installed = {
i.id
for i in self.read_installed(disk_loader=None).installed
} if filter_installed else None
for line in file.text.split('\n'):
if line:
if limit <= 0 or len(res) < limit:
sug = line.split('=')
appid = sug[1].strip()
if installed and appid in installed:
continue
priority = SuggestionPriority(int(sug[0]))
cached_sug = self.suggestions_cache.get(appid)
if cached_sug:
res.append(cached_sug)
else:
app_json = flatpak.search(cli_version,
appid,
remote_level,
app_id=True)
if app_json:
model = PackageSuggestion(
self._map_to_model(app_json[0], False,
None), priority)
self.suggestions_cache.add(appid, model)
res.append(model)
else:
break
res.sort(key=lambda s: s.priority.value, reverse=True)
return res
def list_suggestions(self, limit: int) -> List[PackageSuggestion]:
cli_version = flatpak.get_version()
res = []
sugs = [(i, p) for i, p in suggestions.ALL.items()]
sugs.sort(key=lambda t: t[1].value, reverse=True)
for sug in sugs:
if limit <= 0 or len(res) < limit:
app_json = flatpak.search(cli_version, sug[0], app_id=True)
if app_json:
res.append(
PackageSuggestion(
self._map_to_model(app_json[0], False, None),
sug[1]))
else:
break
res.sort(key=lambda s: s.priority.value, reverse=True)
return res