Use logger instead of print to track eval

tests/eval_frames.py

@@ -1,5 +1,6 @@
 import concurrent.futures
 import json
+import logging
 import os
 import time
 from typing import Any, Dict
@@ -8,6 +9,12 @@ import pandas as pd
 import requests
 from datasets import load_dataset
 
+from khoj.utils.helpers import timer
+
+# Configure root logger
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(message)s")
+logger = logging.getLogger(__name__)
+
 # Configuration
 KHOJ_URL = os.getenv("KHOJ_URL", "http://localhost:42110")
 KHOJ_CHAT_API_URL = f"{KHOJ_URL}/api/chat"
@@ -35,7 +42,7 @@ def load_frames_dataset():
         return dataset["test"][: int(SAMPLE_SIZE)] if SAMPLE_SIZE else dataset["test"]
 
     except Exception as e:
-        print(f"Error loading dataset: {e}")
+        logger.error(f"Error loading dataset: {e}")
         return None
 
 
@@ -45,12 +52,15 @@ def get_agent_response(prompt: str) -> str:
         response = requests.post(
             KHOJ_CHAT_API_URL,
             headers={"Content-Type": "application/json", "Authorization": f"Bearer {KHOJ_API_KEY}"},
-            json={"q": prompt},
+            json={
+                "q": prompt,
+                "create_new": True,
+            },
         )
         response.raise_for_status()
         return response.json().get("response", "")
     except Exception as e:
-        print(f"Error getting agent response: {e}")
+        logger.error(f"Error getting agent response: {e}")
         return ""
 
 
@@ -87,14 +97,14 @@ def evaluate_response(query: str, agent_response: str, ground_truth: str) -> Dic
             "explanation": eval_response.get("explanation", ""),
         }
     except Exception as e:
-        print(f"Error in evaluation: {e}")
+        logger.error(f"Error in evaluation: {e}")
         return {"decision": "FALSE", "explanation": f"Evaluation failed: {str(e)}"}
 
 
 def process_batch(batch, counter, results, dataset_length):
     for prompt, answer, reasoning_type in batch:
         counter += 1
-        print(f"Processing example: {counter}/{dataset_length}")
+        logger.info(f"Processing example: {counter}/{dataset_length}")
 
         # Trigger research mode if enabled
         prompt = f"/{KHOJ_MODE} {prompt}" if KHOJ_MODE else prompt
@@ -119,9 +129,10 @@ def process_batch(batch, counter, results, dataset_length):
         )
 
         # Color the decision based on its value
-        decision_color = "green" if evaluation["decision"] == True else "red"
+        decision = evaluation["decision"]
-        colored_decision = color_text(evaluation["decision"], decision_color)
+        decision_color = "green" if decision == True else "red"
-        print(
+        colored_decision = color_text(str(decision), decision_color)
+        logger.info(
             f'Decision: {colored_decision}\nQuestion: {prompt}\nExpected Answer: {answer}\nAgent Answer: {agent_response}\nExplanation: {evaluation["explanation"]}\n'
         )
 
@@ -140,6 +151,7 @@ def clean_json(response: str):
 
 def main():
     # Load dataset
+    with timer("Loaded dataset in", logger):
         dataset = load_frames_dataset()
     if dataset is None:
         return
@@ -174,9 +186,9 @@ def main():
     df.to_csv("frames_evaluation_results.csv", index=False)
 
     # Print summary
-    print(f"\nOverall Accuracy: {accuracy:.2%}")
+    logger.info(f"\nOverall Accuracy: {accuracy:.2%}")
-    print("\nAccuracy by Reasoning Type:")
+    logger.info("\nAccuracy by Reasoning Type:")
-    print(reasoning_type_accuracy)
+    logger.info(reasoning_type_accuracy)
 
 
 if __name__ == "__main__":
@@ -191,4 +203,5 @@ if __name__ == "__main__":
     Run the script using the following command:
     KHOJ_MODE="research" GEMINI_API_KEY="<your_gemini_api_key>" python eval_frames.py
     """
+    with timer("Ran eval in", logger):
         main()