cleaned up the read me and readability of app/helpers

README.md

@@ -10,23 +10,7 @@ pinned: false
 ---
 # Search Arena
 
-**Search Arena** is a comprehensive platform designed to rigorously evaluate and compare search-based web agents. Leveraging a variety of metrics, Search Arena ensures that users can identify the most effective solutions for their needs.
-
-## Key Features
-
-- **Output Evaluations:** Analyze the quality and relevance of search results.
-- **Perplexity:** Measure the predictive uncertainty of language models used by the agents.
-- **Exa (Exhaustiveness Analysis):** Assess the breadth and depth of search coverage.
-- **Multi-Agent Comparison:** Compare multiple agents side-by-side.
-- **Customizable Benchmarks:** Define specific benchmarks and criteria for evaluation.
-- **User Feedback Integration:** Incorporate user feedback to improve agent performance.
-- **Performance Metrics:** Detailed reports on response time, precision, recall, and F1 score.
-
-## Benefits
-
-- **Enhanced Decision-Making:** Make informed decisions with clear, data-driven evaluations.
-- **Optimization:** Help developers optimize their search agents.
-- **Innovation:** Foster innovation by promoting the best-performing search technologies.
+**Search Arena** is a comprehensive platform designed to evaluate and compare search-based web agents. Leveraging a variety of metrics, Search Arena ensures that users can identify the most effective solutions for their needs.
 
 ## Getting Started
 
@@ -93,17 +77,8 @@ Follow these steps to set up the project on your local machine.
 ### Usage
 
 - Follow the on-screen instructions to evaluate and compare search-based web agents.
-- Customize benchmarks and criteria as needed.
-- Analyze detailed reports and visualizations to make informed decisions.
 
-### Contributing
-
-We welcome contributions! Please read our [Contributing Guide](CONTRIBUTING.md) to get started.
 
 ### License
 
 This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
-
-### Contact
-
-If you have any questions, feel free to open an issue or contact us at [[email protected]](mailto:[email protected]).

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import random
 from helpers import query_you_com, query_tavily, query_perplexity, query_brave
 from provider_info import search_providers
-# from mongod_db import MongoDBHandler
+from mongod_db import MongoDBHandler
 # from swarms.utils.loguru_logger import logger
 import time
 
@@ -47,20 +47,23 @@ def ProcessQuestion(question):
     answer_b = selected_functions[1](question)
     
     # Log into mongodb
-    # try: 
-    #     logger.info(f"Logging question: {question}")
-    #     mongo.add(
-    #         {
-    #             "question": question,
-    #             "answer_a": answer_a,
-    #             "answer_b": answer_b,
-    #             "selected_functions": [f.__name__ for f in selected_functions],
-    #             "query_time": time.time(),
-    #         }
-    #     )
-    #     logger.info("Successfully logged into mongodb")
-    # except Exception as e:
-    #     logger.error(f"Error logging into mongodb: {e}")
+    mongo = MongoDBHandler()
+    
+    try: 
+        # logger.info(f"Logging question: {question}")
+        mongo.add(
+            {
+                "question": question,
+                "answer_a": answer_a,
+                "answer_b": answer_b,
+                "selected_functions": [f.__name__ for f in selected_functions],
+                "query_time": time.time(),
+            }
+        )
+        # logger.info("Successfully logged into mongodb")
+    except Exception as e:
+        # logger.error(f"Error logging into mongodb: {e}")
+        print("Error logging into mongodb: {e}")
 
     return answer_a, answer_b, selected_functions
 

helpers.py

@@ -1,6 +1,7 @@
 import requests
 from dotenv import load_dotenv
 from typing import Optional
+from brave_ai import BraveAIWrapper
 import os
 
 # Load environment variables from .env file
@@ -89,8 +90,6 @@ def ProcessQuestion(question, model):
         return "Model not supported"
 
 
-from brave_ai import BraveAIWrapper
-
 def query_brave(query: str) -> Optional[str]:
     """
     Get a summary for the given query using BraveAIWrapper.