fixed inline #82

IEX_Symbol.py

@@ -10,7 +10,6 @@ from typing import List, Optional, Tuple
 import pandas as pd
 import requests as r
 import schedule
-from fuzzywuzzy import fuzz
 
 from Symbol import Stock
 
@@ -141,46 +140,6 @@ class IEX_Symbol:
                 + " Please check the status page for more information. https://status.iexapis.com"
             )
 
-    def search_symbols(self, search: str) -> List[Tuple[str, str]]:
-        """Performs a fuzzy search to find stock symbols closest to a search term.
-
-        Parameters
-        ----------
-        search : str
-            String used to search, could be a company name or something close to the companies stock ticker.
-
-        Returns
-        -------
-        List[tuple[str, str]]
-            A list tuples of every stock sorted in order of how well they match. Each tuple contains: (Symbol, Issue Name).
-        """
-
-        schedule.run_pending()
-        search = search.lower()
-        try:  # https://stackoverflow.com/a/3845776/8774114
-            return self.searched_symbols[search]
-        except KeyError:
-            pass
-
-        symbols = self.symbol_list
-        symbols["Match"] = symbols.apply(
-            lambda x: fuzz.ratio(search, f"{x['symbol']}".lower()),
-            axis=1,
-        )
-
-        symbols.sort_values(by="Match", ascending=False, inplace=True)
-        if symbols["Match"].head().sum() < 300:
-            symbols["Match"] = symbols.apply(
-                lambda x: fuzz.partial_ratio(search, x["name"].lower()),
-                axis=1,
-            )
-
-            symbols.sort_values(by="Match", ascending=False, inplace=True)
-        symbols = symbols.head(10)
-        symbol_list = list(zip(list(symbols["symbol"]), list(symbols["description"])))
-        self.searched_symbols[search] = symbol_list
-        return symbol_list
-
     def price_reply(self, symbol: Stock) -> str:
         """Returns price movement of Stock for the last market day, or after hours.
 

bot.py

@@ -261,8 +261,8 @@ def information(update: Update, context: CallbackContext):
 
 def search(update: Update, context: CallbackContext):
     """
-    Uses fuzzy search on full list of stocks and crypto names
+    Searches on full list of stocks and crypto descriptions
-        and descriptions then returns the top matches in order.
+    then returns the top matches in order of smallest symbol name length.
     """
     info(f"Search command ran by {update.message.chat.username}")
     message = update.message.text.replace("/search ", "")
@@ -275,11 +275,13 @@ def search(update: Update, context: CallbackContext):
         return
 
     context.bot.send_chat_action(chat_id=chat_id, action=telegram.ChatAction.TYPING)
-    queries = s.search_symbols(message)[:10]
+    queries = s.inline_search(message, matches=10)
-    if queries:
+    if not queries.empty:
-        reply = "*Search Results:*\n`$ticker: Company Name`\n`" + ("-" * 21) + "`\n"
+        reply = "*Search Results:*\n`$ticker` : Company Name\n`" + ("-" * 21) + "`\n"
-        for query in queries:
+        for _, query in queries.iterrows():
-            reply += "`" + query[1] + "`\n"
+            desc = query["description"]
+            reply += "`" + desc.replace(": ", "` : ") + "\n"
+        print(reply)
         update.message.reply_text(
             text=reply,
             parse_mode=telegram.ParseMode.MARKDOWN,
@@ -466,28 +468,23 @@ def inline_query(update: Update, context: CallbackContext):
     Handles inline query. Searches by looking if query is contained
         in the symbol and returns matches in alphabetical order.
     """
-    info(f"Inline command ran by {update.message.chat.username}")
+    # info(f"Inline command ran by {update.message.chat.username}")
     info(f"Query: {update.inline_query.query}")
-    matches = s.inline_search(update.inline_query.query)[:5]
 
-    symbols = " ".join([match[1].split(":")[0] for match in matches])
+    matches = s.inline_search(update.inline_query.query)
-    prices = s.batch_price_reply(s.find_symbols(symbols))
 
     results = []
-    for match, price in zip(matches, prices):
+    for _, row in matches.iterrows():
-        try:
+        print(row)
-            results.append(
+        results.append(
-                InlineQueryResultArticle(
+            InlineQueryResultArticle(
-                    str(uuid4()),
+                str(uuid4()),
-                    title=match[1],
+                title=row["description"],
-                    input_message_content=InputTextMessageContent(
+                input_message_content=InputTextMessageContent(
-                        price, parse_mode=telegram.ParseMode.MARKDOWN
+                    row["price_reply"], parse_mode=telegram.ParseMode.MARKDOWN
-                    ),
+                ),
-                )
             )
-        except TypeError:
+        )
-            warning(f"{match} caused error in inline query.")
-            pass
 
         if len(results) == 5:
             update.inline_query.answer(results)
@@ -568,6 +565,7 @@ def main():
     dp.add_handler(CommandHandler("random", rand_pick))
     dp.add_handler(CommandHandler("donate", donate))
     dp.add_handler(CommandHandler("status", status))
+    dp.add_handler(CommandHandler("inline", inline_query))
 
     # Charting can be slow so they run async.
     dp.add_handler(CommandHandler("intra", intra, run_async=True))

cg_Crypto.py

@@ -9,7 +9,6 @@ from typing import List, Optional, Tuple
 import pandas as pd
 import requests as r
 import schedule
-from fuzzywuzzy import fuzz
 from markdownify import markdownify
 
 from Symbol import Coin
@@ -74,7 +73,7 @@ class cg_Crypto:
             "$$" + symbols["symbol"].str.upper() + ": " + symbols["name"]
         )
         symbols = symbols[["id", "symbol", "name", "description"]]
-        symbols["type_id"] = "$$" + symbols["id"]
+        symbols["type_id"] = "$$" + symbols["symbol"]
 
         self.symbol_list = symbols
         if return_df:
@@ -99,45 +98,6 @@ class cg_Crypto:
         except:
             return f"CoinGecko API returned an error code {status.status_code} in {status.elapsed.total_seconds()} Seconds."
 
-    def search_symbols(self, search: str) -> List[Tuple[str, str]]:
-        """Performs a fuzzy search to find coin symbols closest to a search term.
-
-        Parameters
-        ----------
-        search : str
-            String used to search, could be a company name or something close to the companies coin ticker.
-
-        Returns
-        -------
-        List[tuple[str, str]]
-            A list tuples of every coin sorted in order of how well they match. Each tuple contains: (Symbol, Issue Name).
-        """
-        schedule.run_pending()
-        search = search.lower()
-        try:  # https://stackoverflow.com/a/3845776/8774114
-            return self.searched_symbols[search]
-        except KeyError:
-            pass
-
-        symbols = self.symbol_list
-        symbols["Match"] = symbols.apply(
-            lambda x: fuzz.ratio(search, f"{x['symbol']}".lower()),
-            axis=1,
-        )
-
-        symbols.sort_values(by="Match", ascending=False, inplace=True)
-        if symbols["Match"].head().sum() < 300:
-            symbols["Match"] = symbols.apply(
-                lambda x: fuzz.partial_ratio(search, x["name"].lower()),
-                axis=1,
-            )
-
-            symbols.sort_values(by="Match", ascending=False, inplace=True)
-        symbols = symbols.head(10)
-        symbol_list = list(zip(list(symbols["symbol"]), list(symbols["description"])))
-        self.searched_symbols[search] = symbol_list
-        return symbol_list
-
     def price_reply(self, coin: Coin) -> str:
         """Returns current market price or after hours if its available for a given coin symbol.
 

requirements.txt

@@ -1,8 +1,6 @@
 python-telegram-bot==13.5
 requests==2.25.1
 pandas==1.2.1
-fuzzywuzzy==0.18.0
-python-Levenshtein==0.12.1
 schedule==1.0.0
 mplfinance==0.12.7a5
 markdownify==0.6.5

symbol_router.py

@@ -9,7 +9,6 @@ from logging import critical, debug, error, info, warning
 import pandas as pd
 import schedule
 from cachetools import TTLCache, cached
-from fuzzywuzzy import fuzz
 
 from cg_Crypto import cg_Crypto
 from IEX_Symbol import IEX_Symbol
@@ -19,7 +18,6 @@ from Symbol import Coin, Stock, Symbol
 class Router:
     STOCK_REGEX = "(?:^|[^\\$])\\$([a-zA-Z.]{1,6})"
     CRYPTO_REGEX = "[$]{2}([a-zA-Z]{1,20})"
-    searched_symbols = {}
     trending_count = {}
 
     def __init__(self):
@@ -110,45 +108,7 @@ class Router:
 
         return stats
 
-    def search_symbols(self, search: str) -> list[tuple[str, str]]:
+    def inline_search(self, search: str, matches: int = 5) -> pd.DataFrame:
-        """Performs a fuzzy search to find stock symbols closest to a search term.
-
-        Parameters
-        ----------
-        search : str
-            String used to search, could be a company name or something close to the companies stock ticker.
-
-        Returns
-        -------
-        list[tuple[str, str]]
-            A list tuples of every stock sorted in order of how well they match.
-                Each tuple contains: (Symbol, Issue Name).
-        """
-
-        df = pd.concat([self.stock.symbol_list, self.crypto.symbol_list])
-
-        search = search.lower()
-
-        df["Match"] = df.apply(
-            lambda x: fuzz.ratio(search, f"{x['symbol']}".lower()),
-            axis=1,
-        )
-
-        df.sort_values(by="Match", ascending=False, inplace=True)
-        # if df["Match"].head().sum() < 300:
-        #     df["Match"] = df.apply(
-        #         lambda x: fuzz.partial_ratio(search, x["name"].lower()),
-        #         axis=1,
-        #     )
-
-        #     df.sort_values(by="Match", ascending=False, inplace=True)
-
-        symbols = df.head(20)
-        symbol_list = list(zip(list(symbols["symbol"]), list(symbols["description"])))
-        self.searched_symbols[search] = symbol_list
-        return symbol_list
-
-    def inline_search(self, search: str) -> list[tuple[str, str]]:
         """Searches based on the shortest symbol that contains the same string as the search.
         Should be very fast compared to a fuzzy search.
 
@@ -165,16 +125,16 @@ class Router:
 
         df = pd.concat([self.stock.symbol_list, self.crypto.symbol_list])
 
-        search = search.lower()
+        df = df[
+            df["description"].str.contains(search, regex=False, case=False)
+        ].sort_values(by="type_id", key=lambda x: x.str.len())
 
-        df = df[df["type_id"].str.contains(search, regex=False)].sort_values(
+        symbols = df.head(matches)
-            by="type_id", key=lambda x: x.str.len()
+        symbols["price_reply"] = symbols["type_id"].apply(
+            lambda sym: self.price_reply(self.find_symbols(sym))[0]
         )
 
-        symbols = df.head(20)
+        return symbols
-        symbol_list = list(zip(list(symbols["symbol"]), list(symbols["description"])))
-        self.searched_symbols[search] = symbol_list
-        return symbol_list
 
     def price_reply(self, symbols: list[Symbol]) -> list[str]:
         """Returns current market price or after hours if its available for a given stock symbol.