Update app.py

app.py

@@ -11,6 +11,7 @@ from functools import lru_cache  # Added: For caching search results
 from youtube_transcript_api import YouTubeTranscriptApi
 import re
 
+
 class YouTubeVideoTool:
     def __init__(self):
         self.name = "youtube_video_tool"
@@ -41,6 +42,8 @@ class YouTubeVideoTool:
         except Exception as e:
             return f"Error processing YouTube video: {str(e)}"
     
+    
+    
     def _extract_video_id(self, url_or_id):
         """Extract YouTube video ID from various URL formats or return the ID if already provided."""
         # Handle direct video ID
@@ -78,13 +81,18 @@ def cached_search(query):
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
 class BasicAgent:
+    
+    
     def __init__(self, model=None, tools=None):
         self.model = model
         self.tools = tools if tools is not None else []
         self.history = []
-        print("BasicAgent initialized.")
+        print(f"BasicAgent initialized with model: {model} and {len(self.tools)} tools.")
         
+    
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
         # Implement your agent logic here using self.model and self.tools
@@ -92,6 +100,8 @@ class BasicAgent:
         print(f"Agent returning answer: {final_answer[:50]}...")
         return final_answer
 
+    
+    
     def process_question(self, question:str) -> str:
         try:
             # Check if this is a request about a YouTube video
@@ -111,7 +121,7 @@ class BasicAgent:
                 return self._formulate_direct_answer(relevant_info, question)
             else:
                 # Use regular search
-                search_results = cached_search(question) if self.tools and search_tool in self.tools else "No search results available."
+                search_results = cached_search(question) if any(isinstance(tool, DuckDuckGoSearchTool) for tool in self.tools) else "No search results available."
                 relevant_info = self._extract_key_info(search_results, question)
                 return self._formulate_direct_answer(relevant_info, question)
         except Exception as e:
@@ -125,6 +135,8 @@ class BasicAgent:
                     return self._get_fallback_answer(question)
             return self._get_fallback_answer(question)
     
+    
+    
     def _extract_key_info(self, search_results, question):
         # Split results into sentences and find most relevant
         sentences = search_results.split('. ')
@@ -141,10 +153,15 @@ class BasicAgent:
         # Fallback to first few sentences
         return '. '.join(sentences[:2])
 
+    
+    
     def _formulate_direct_answer(self, relevant_info, question):
-        # Format as direct, concise answer
+        if self.model:
+            return f"Based on the search: {relevant_info}"
         return relevant_info
     
+    
+    
     def _get_fallback_answer(self, question):
         return f"Based on the information available, I cannot provide a specific answer to your question about {question.split()[0:3]}..."
 
@@ -169,6 +186,8 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
+
+    
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
         agent = BasicAgent(model= 'gemini/gemini-2.0-flash-exp', tools=[search_tool, visit_webpage, youtube_tool])
@@ -179,6 +198,8 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
+
+    
     # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
     try:
@@ -200,11 +221,13 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
+
+    
     # 3. Run your Agent
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+    for idx, item in enumerate(questions_data):
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
@@ -224,11 +247,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
+
+    
     # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
+
+    
     # 5. Submit
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
@@ -273,6 +300,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         return status_message, results_df
 
 
+    
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")