black formatting

Author: amanjaiswal73892

src/agentlab/llm/tracking.py

@@ -163,10 +163,10 @@ def __call__(self, *args, **kwargs):
         response = self._call_api(*args, **kwargs)
 
         usage = dict(getattr(response, "usage", {}))
-        if 'prompt_tokens_details' in usage:
-            usage['cached_tokens'] = usage['prompt_token_details'].cached_tokens
-        if 'input_tokens_details' in usage:
-            usage['cached_tokens'] = usage['input_tokens_details'].cached_tokens
+        if "prompt_tokens_details" in usage:
+            usage["cached_tokens"] = usage["prompt_token_details"].cached_tokens
+        if "input_tokens_details" in usage:
+            usage["cached_tokens"] = usage["input_tokens_details"].cached_tokens
         usage = {f"usage_{k}": v for k, v in usage.items() if isinstance(v, (int, float))}
         usage |= {"n_api_calls": 1}
         usage |= {"effective_cost": self.get_effective_cost(response)}
@@ -306,21 +306,21 @@ def get_effective_cost_from_openai_api(self, response) -> float:
         if usage is None:
             logging.warning("No usage information found in the response. Defaulting cost to 0.0.")
             return 0.0
-        api_type = 'chatcompletion' if hasattr(usage, "prompt_tokens_details") else 'response'
-        if api_type == 'chatcompletion':
+        api_type = "chatcompletion" if hasattr(usage, "prompt_tokens_details") else "response"
+        if api_type == "chatcompletion":
             total_input_tokens = usage.prompt_tokens
             output_tokens = usage.completion_tokens
             cached_input_tokens = usage.prompt_tokens_details.cached_tokens
             non_cached_input_tokens = total_input_tokens - cached_input_tokens
-        elif api_type == 'response':
+        elif api_type == "response":
             total_input_tokens = usage.input_tokens
             output_tokens = usage.output_tokens
             cached_input_tokens = usage.input_tokens_details.cached_tokens
             non_cached_input_tokens = total_input_tokens - cached_input_tokens
         else:
             logging.warning(f"Unsupported API type: {api_type}. Defaulting cost to 0.0.")
             return 0.0
-        
+
         cache_read_cost = self.input_cost * OPENAI_CACHE_PRICING_FACTOR["cache_read_tokens"]
         effective_cost = (
             self.input_cost * non_cached_input_tokens