Update app.py

app.py

@@ -33,6 +33,16 @@ start_backup_thread()
 def get_available_models():
     return [model[0] for model in arena_config.APPROVED_MODELS]
 
+# Function to get recent opponents for a model
+recent_opponents = {}
+
+def update_recent_opponents(model_a, model_b):
+    recent_opponents.setdefault(model_a, []).append(model_b)
+    recent_opponents.setdefault(model_b, []).append(model_a)
+    # Limit history to last 5 opponents
+    recent_opponents[model_a] = recent_opponents[model_a][-5:]
+    recent_opponents[model_b] = recent_opponents[model_b][-5:]
+
 # Function to call Ollama API with caching
 @lru_cache(maxsize=100)
 def call_ollama_api(model, prompt):
@@ -84,9 +94,20 @@ def generate_responses(prompt):
     # Select the first model (least battles)
     model_a = sorted_models[0]
     
+    # Filter out recent opponents for model_a
+    potential_opponents = [m for m in sorted_models[1:] if m not in recent_opponents.get(model_a, [])]
+    
+    # If no potential opponents left, reset recent opponents for model_a
+    if not potential_opponents:
+        recent_opponents[model_a] = []
+        potential_opponents = sorted_models[1:]
+    
     # For the second model, use weighted random selection
-    weights = [1 / (battle_counts.get(m, 1) + 1) for m in sorted_models[1:]]
-    model_b = random.choices(sorted_models[1:], weights=weights, k=1)[0]
+    weights = [1 / (battle_counts.get(m, 1) + 1) for m in potential_opponents]
+    model_b = random.choices(potential_opponents, weights=weights, k=1)[0]
+    
+    # Update recent opponents
+    update_recent_opponents(model_a, model_b)
     
     model_a_response = call_ollama_api(model_a, prompt)
     model_b_response = call_ollama_api(model_b, prompt)
@@ -555,4 +576,4 @@ with gr.Blocks(css="""
 if __name__ == "__main__":
     # Initialize ELO ratings before launching the app
     ensure_elo_ratings_initialized()
-    demo.launch(show_api=False)
+    demo.launch(show_api=False)