Update app.py

app.py

@@ -5,6 +5,9 @@ import inspect
 import pandas as pd
 import smolagents
 from smolagents import DuckDuckGoSearchTool, VisitWebpageTool
+import time  # Added: For sleep functionality
+from functools import lru_cache  # Added: For caching search results
+
 
 search_tool = DuckDuckGoSearchTool()
 visit_webpage = VisitWebpageTool()
@@ -13,6 +16,11 @@ visit_webpage = VisitWebpageTool()
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+# Cache Wrapper
+@lru_cache(maxsize=100)
+def cached_search(query):
+    return search_tool(query)
+
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
@@ -29,8 +37,8 @@ class BasicAgent:
         print(f"Agent returning answer: {final_answer[:50]}...")
         return final_answer
 
-    def _process_question (self, question:str) -> str:
-        search_results = search_tool(question) if self.tools and search_tool in self.tools else "No search results available."
+    def process_question (self, question:str) -> str:
+        search_results = cached_search(question) if self.tools and search_tool in self.tools else "No search results available."
         return f"Based on my analysis of '{question}', I found: {search_results[:500]}..."
         
 
@@ -98,6 +106,8 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             submitted_answer = agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+            if idx % 5 == 0 and idx > 0:  # Added: Add delay every 5 questions
+                time.sleep(1)  # Wait 1 second between batches to avoid rate limiting
         except Exception as e:
              print(f"Error running agent on task {task_id}: {e}")
              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})