Update tools.py

added 'self' keyword to access variables

tools.py

@@ -34,18 +34,18 @@ class PokemonAdvisorTools():
 
     def list_card_names(self, name_query):
         """Retrieves a list of matching card names for the user's query."""
-        prod_names_match = process.extract(name_query, ALL_PROD_NAMES, scorer=fuzz.WRatio, limit=5)
+        prod_names_match = process.extract(name_query, self.ALL_PROD_NAMES, scorer=fuzz.WRatio, limit=5)
         return [name[0].replace("_", " ") for name in prod_names_match]
         
     def get_card_info(self, name_query):
         """Retrieves core financial and meta data for a single card."""
-        prod_name = process.extractOne(name_query, ALL_PROD_NAMES, scorer=fuzz.WRatio)[0]
-        card_df = knowledge_base_latest[knowledge_base_latest["prod_name"] == prod_name]
+        prod_name = process.extractOne(name_query, self.ALL_PROD_NAMES, scorer=fuzz.WRatio)[0]
+        card_df = self.knowledge_base_latest[self.knowledge_base_latest["prod_name"] == prod_name]
         return card_df.to_dict(orient="records")[0]
 
     def find_grading_opportunities(self, max_price=50, min_profit=20):
         """Scans the market for cards currently offering the highest raw-to-graded profit."""
-        profitable_grades = knowledge_base_latest[knowledge_base_latest["is_grade_profitable"] == True]
+        profitable_grades = self.knowledge_base_latest[self.knowledge_base_latest["is_grade_profitable"] == True]
         profitable_grades = profitable_grades[profitable_grades["used_price"] <= max_price]
         min_profit_grades = profitable_grades[profitable_grades["grade_profit"] >= min_profit]
         min_profit_grades = min_profit_grades.sort_values(
@@ -65,7 +65,7 @@ class PokemonAdvisorTools():
 
     def get_market_movers(self, sort_by="uptrend", interval=6):
         """Analyzes the current market and identifies the top 10 cards experiencing the largest positive (uptrend) or negative (downtrend) price movement over the last 6 months."""
-        market_move_data = knowledge_base_latest.sort(by=f"trend_{interval}", ascending=(not sort_by=="uptrend")).head(10)
+        market_move_data = self.knowledge_base_latest.sort(by=f"trend_{interval}", ascending=(not sort_by=="uptrend")).head(10)
         output_columns = ["prod_name", "used_price", "graded_price"]
         market_move_data = market_move_data[output_columns]
         return market_move_data.to_dict(orient="records")
@@ -136,13 +136,13 @@ class PokemonAdvisorTools():
         """Scans the latest market snapshot to identify the top 20 cards that have experienced a sudden, significant price increase (jump-up) in either the used or graded market."""
         market_type = market_type.lower().strip()
         if market_type == "used":
-            jump_data = knowledge_base_latest[knowledge_base_latest["used_jump_up"] == True]
+            jump_data = self.knowledge_base_latest[self.knowledge_base_latest["used_jump_up"] == True]
             jump_data = jump_data.sort_values("used_price", ascending=False).head(20)
             output_columns = ["prod_name", "set_name", "used_price"]
             return jump_data[output_columns].to_dict(orient="records")
 
         elif market_type == "graded":
-            jump_data = knowledge_base_latest[knowledge_base_latest["graded_jump_up"] == True]
+            jump_data = self.knowledge_base_latest[self.knowledge_base_latest["graded_jump_up"] == True]
             jump_data = jump_data.sort_values("graded_price", ascending=False).head(20)
             output_columns = ["prod_name", "set_name", "graded_price"]
             return jump_data[output_columns].to_dict(orient="records")
@@ -153,13 +153,13 @@ class PokemonAdvisorTools():
 
     def find_cards_by_artist(self, artist_name):
         """Identifies the top 20 cards drawn by a specified artist that currently present the highest raw-to-graded arbitrage profit opportunity."""
-        artist_match = process.extractOne(artist_name, ALL_ARTIST_NAMES, scorer=fuzz.WRatio)
+        artist_match = process.extractOne(artist_name, self.ALL_ARTIST_NAMES, scorer=fuzz.WRatio)
 
         if not artist_match or artist_match[1] < 75:
             return {"error": f"Artist '{artist_name}' not found or matched with low confidence."}
 
         artist_name_match = artist_match[0]
-        artist_card_data = knowledge_base_latest[knowledge_base_latest["artist"] == artist_name_match]
+        artist_card_data = self.knowledge_base_latest[self.knowledge_base_latest["artist"] == artist_name_match]
         profitable_cards = artist_card_data[artist_card_data["is_grade_profitable"] == True]
         profitable_cards = profitable_cards.sort_values(by="grade_profit", ascending=False).head(20)
 
@@ -182,8 +182,8 @@ class PokemonAdvisorTools():
 
     def analyze_set_performance(self, set_name):
         """Provides a macro-level financial summary of a specific set, including its overall average 6-month trend, average grading profit, and the most expensive (chase) card."""
-        set_name_match = process.extractOne(set_name.lower(), ALL_SET_NAMES, scorer=fuzz.WRatio)[0]
-        set_card_data = knowledge_base_latest[knowledge_base_latest["set_name"] == set_name_match]
+        set_name_match = process.extractOne(set_name.lower(), self.ALL_SET_NAMES, scorer=fuzz.WRatio)[0]
+        set_card_data = self.knowledge_base_latest[self.knowledge_base_latest["set_name"] == set_name_match]
         total_cards = len(set_card_data)
         avg_trend_6 = set_card_data["used_trend_6"].mean()
         avg_grade_profit = set_card_data["grade_profit"].mean()