add a query source that does not need a language model

Idea taken from TheNetsky/Microsoft-Rewards-Script, which builds search terms
from public feeds rather than a model. No code from it: that project is
GPL-3.0 and this one is MIT, so only the approach crosses over.

The LLM has exactly two call sites here, both producing a short string to type
into Bing. Everything the dependency costs, an Ollama account, cloud usage and
the provider work in #15, is paid for search strings. Three keyless sources
answer the same question:

  Google Trends RSS    queries people are actually typing right now
  Wikipedia most-read  topic seeds when trends is unavailable
  Bing autosuggest     expands a seed into related queries

Autosuggest is what makes the chaining work. Asking Bing what follows a term
returns queries Bing already expects, which is nearer to what the prompt in
llm_utils was reaching for than a model guessing unaided.

Selected with QUERY_SOURCE=trends. The default stays llm, so no existing setup
changes. stdlib only, no new dependencies.

Measured against the LLM on the same cards from a live account:

  card                     llm                                    trends
  airport parking          best rates airport parking reservations reserve airport parking best rates
  checking vs savings      compare checking vs savings accounts    compare checking savings account options
  cruise deals             best cruise deals and destinations      cruise deals destinations

Verified live with OLLAMA_HOST pointed at a dead port, so nothing could reach
a model: five queries generated from feeds and three typed into Bing, each
landing on a real results page.

Every source degrades to an empty list rather than raising, and both entry
points fall back, to the trimmed task description and to nouns.txt. A search
that does not happen costs points; a run that dies costs the rest of the day.

src/rewards_tasks.py

@@ -10,7 +10,7 @@ from selenium.webdriver.remote.webelement import WebElement
 from selenium.webdriver.common.action_chains import ActionChains
 from selenium.common.exceptions import StaleElementReferenceException, TimeoutException, NoSuchElementException
 import tab_utils
-import llm_utils
+import queries
 import mouse_trajectory
 import mimic_typing
 import element_selectors
@@ -93,7 +93,7 @@ class RewardsTaskUtils:
 
 		for card in explore_on_bing_links:
 			desc = self.elements.extract_card_descriptions(card)
-			query = llm_utils.get_search_query_from_task_description(desc)
+			query = queries.search_query_for_task(desc)
 
 			self.move_to_and_click(card)
 			self.tab_utils.switch_to_other_tab()
@@ -220,9 +220,7 @@ class RewardsTaskUtils:
 		# search bar should be auto-focused
 
 		for i, query in enumerate(
-			llm_utils.get_related_search_queries(
-				llm_utils.get_random_noun(), num_queries=count
-			)
+			queries.related_queries(count)
 		):
 			self.keyboard.send_keys(f"{query} -noai{Keys.ENTER}")