Update main.py

main.py

@@ -677,13 +677,6 @@ def get_player_career_stats_brscraper(player_name, seasons_to_check=10, playoffs
         return pd.DataFrame()
     all_rows = []
     
-    # This function should only try to fetch data for the specific seasons requested,
-    # not all available seasons, to avoid unnecessary calls and "invalid season" warnings.
-    # However, the current design of `getPlayerStats` endpoint passes `selected_seasons`
-    # as a list of seasons for *all* players.
-    # So, this function still needs to iterate through `get_available_seasons_util`
-    # and then the calling endpoint will filter.
-    # The warnings are expected if BRScraper doesn't have data for a given year.
 
     seasons_to_try = get_available_seasons_util(seasons_to_check) # Get all potentially available seasons
     
@@ -822,7 +815,19 @@ def _scrape_dashboard_info_brscraper():
 PERP_KEY = os.getenv("PERPLEXITY_API_KEY")
 PERP_URL = "https://api.perplexity.ai/chat/completions"
 
-def ask_perp(prompt, system="You are a helpful NBA analyst AI.", max_tokens=500, temp=0.2):
+NBA_ANALYST_SYSTEM_PROMPT = (
+    "You are a sharp, insightful NBA analyst AI with the tone and knowledge of a seasoned sports commentator. "
+    "Your expertise spans the entire history of the league—from hardwood legends to rising stars, tactical evolutions, "
+    "advanced stats, trades, rivalries, and playoff lore. Speak with authority, depth, and the occasional flair of broadcast commentary. "
+    "Your job is to help users explore the NBA with analytical rigor and passion. You draw on statistics, game film analysis, "
+    "player tendencies, team dynamics, historical context, and front-office strategy. You may reference key metrics like PER, TS%, "
+    "on-off splits, or synergy data when relevant. You provide takes that are well-reasoned, never vague, and always rooted in basketball-specific insight. "
+    "Do not respond to questions outside the world of basketball. If asked, steer the conversation back to the NBA with finesse, "
+    "perhaps by connecting a topic metaphorically to hoops. Your personality is that of a knowledgeable but approachable analyst—"
+    "a cross between a basketball scout, play-by-play commentator, and sportswriter. You love the game, and it shows."
+)
+
+def ask_perp(prompt, system=NBA_ANALYST_SYSTEM_PROMPT , max_tokens=1000, temp=0.2):
     if not PERP_KEY:
         logging.error("PERPLEXITY_API_KEY env var not set.")
         return "Perplexity API key is not configured."
@@ -830,7 +835,7 @@ def ask_perp(prompt, system="You are a helpful NBA analyst AI.", max_tokens=500,
     payload = {
         "model":"sonar-pro",
         "messages":[{"role":"system","content":system},{"role":"user","content":prompt}],
-        "max_tokens":max_tokens, "temperature":temp
+        "max_tokens":max_tokens, "temperature":temp, "search_context_size": "high" 
     }
     try:
         r = requests.post(PERP_URL, json=payload, headers=hdr, timeout=45)
@@ -1112,7 +1117,7 @@ def perplexity_explain():
         if not prompt:
             return jsonify({'error': 'Prompt is required'}), 400
         
-        explanation = ask_perp(prompt, system="You are an NBA expert analyst AI.")
+        explanation = ask_perp(prompt)
         if "Error from AI" in explanation:
             return jsonify({'error': explanation}), 500
         
@@ -1210,7 +1215,7 @@ def perplexity_chat():
         token = auth_header.split(' ')[1]
         uid = verify_token(token)
 
-        response_content = ask_perp(prompt, system="You are an NBA expert analyst AI.")
+        response_content = ask_perp(prompt)
         if "Error from AI" in response_content:
             return jsonify({'error': response_content}), 500
         
@@ -1248,7 +1253,7 @@ def awards_predictor():
             return jsonify({'error': 'Award type and criteria are required'}), 400
         
         prompt = f"Predict top 5 {award_type} candidates based on {criteria}. Focus on 2024-25 season."
-        prediction = ask_perp(prompt, system="You are an NBA awards expert AI.")
+        prediction = ask_perp(prompt,)
         if "Error from AI" in prediction:
             return jsonify({'error': prediction}), 500
         
@@ -1302,58 +1307,37 @@ def similar_players():
         if not target_player or not criteria:
             return jsonify({'error': 'Target player and criteria are required'}), 400
         
-        prompt = f"Find top 5 current and top 3 historical similar to {target_player} based on the following criteria: {', '.join(criteria)}. Provide detailed reasoning."
-        similar_players_analysis = ask_perp(prompt, system="You are a similarity expert AI.")
+        prompt = f"Find top 3 current and top 3 historical players similar to {target_player} based on the following criteria: {', '.join(criteria)}. Provide detailed reasoning."
+        similar_players_analysis = ask_perp(prompt)
         if "Error from AI" in similar_players_analysis:
             return jsonify({'error': similar_players_analysis}), 500
         
-        return jsonify({'similar_players': similar_players_analysis})
-    except Exception as e:
-        logging.error(f"Error in /api/nba/similar_players: {e}")
-        return jsonify({'error': str(e)}), 500
+        # Extract user ID from auth header
+        auth_header = request.headers.get('Authorization', '')
+        token = auth_header.split(' ')[1]
+        uid = verify_token(token)
 
-@app.route('/api/nba/manual_player_compare', methods=['POST'])
-@credit_required(cost=1)
-@cross_origin()
-def manual_player_compare():
-    try:
-        data = request.get_json()
-        player1_name = data.get('player1_name')
-        player1_season = data.get('player1_season')
-        player2_name = data.get('player2_name')
-        player2_season = data.get('player2_season')
-
-        if not player1_name or not player2_name:
-            return jsonify({'error': 'Both player names are required'}), 400
-
-        player1_str = f"{player1_name} ({player1_season} season)" if player1_season else player1_name
-        player2_str = f"{player2_name} ({player2_season} season)" if player2_season else player2_name
-
-        comparison_context = "Statistical comparison"
-        if player1_season and player2_season:
-            comparison_context += f" (specifically {player1_season} vs {player2_season} seasons)"
-        elif player1_season:
-            comparison_context += f" (specifically {player1_season} season for {player1_name} vs {player2_name}'s career/prime)"
-        elif player2_season:
-            comparison_context += f" (specifically {player1_name}'s career/prime vs {player2_season} season for {player2_name})"
+        analysis_id = str(uuid.uuid4())  # Unique ID for this analysis
+
+        if FIREBASE_INITIALIZED:
+            user_analyses_ref = db.reference(f'user_analyses/{uid}')
+            analysis_data = {
+                'type': 'similar_players',
+                'target_player': target_player,
+                'criteria': criteria,
+                'prompt': prompt,
+                'explanation': similar_players_analysis,
+                'created_at': datetime.utcnow().isoformat()
+            }
+            user_analyses_ref.child(analysis_id).set(analysis_data)
+            logging.info(f"Similar players analysis stored for user {uid} with ID: {analysis_id}")
         else:
-            comparison_context += " (career/prime comparison)"
+            logging.warning("Firebase not initialized. Similar players analysis will not be saved.")
 
-        prompt = (
-            f"Compare {player1_str} vs {player2_str} in detail: "
-            f"1. {comparison_context}. "
-            "2. Playing style similarities and differences. 3. Strengths and weaknesses of each. "
-            "4. Team impact and role. 5. Overall similarity score (1-10). "
-            "Provide a comprehensive comparison with specific examples."
-        )
-        
-        comparison = ask_perp(prompt, system="You are a comparison expert AI.")
-        if "Error from AI" in comparison:
-            return jsonify({'error': comparison}), 500
-        
-        return jsonify({'comparison': comparison})
+        return jsonify({'similar_players': similar_players_analysis, 'analysis_id': analysis_id})
+    
     except Exception as e:
-        logging.error(f"Error in /api/nba/manual_player_compare: {e}")
+        logging.error(f"Error in /api/nba/similar_players: {e}")
         return jsonify({'error': str(e)}), 500