refactor: code organisation + info about local services on the gradio home page

app.py

@@ -1,11 +1,10 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 from dotenv import load_dotenv
 
-from myagent import BasicAgent  # Import your agent class from myagent.py
+from myagent import BasicAgent  # Unused basic single agent
 from multiagents import MultiAgent
 
 from phoenix.otel import register
@@ -100,7 +99,7 @@ def run_and_submit_all(nb_questions: int, profile: gr.OAuthProfile | None):
                 agent_question += f"\n\nFile URL: {file_question_url}"
 
             submitted_answer = agent(agent_question)
-            
+
             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})
         except Exception as e:
@@ -163,6 +162,10 @@ def run_and_submit_all(nb_questions: int, profile: gr.OAuthProfile | None):
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("""
+                *Special Considerations*: Due to limitation issues, this code depend on local search engine and local speech to text model. Both run through docker, see the readme file."
+                One can achieve similar result, by using Google search API and OpenAI Whisper API.
+        """)
     gr.Markdown(
         """
         **Instructions:**

multiagents.py

@@ -2,14 +2,14 @@
 # a multi agent proposal to solve HF agent course final assignment
 import os
 import dotenv
-from smolagents import CodeAgent, ToolCallingAgent
+from smolagents import CodeAgent
 from smolagents import OpenAIServerModel
 from tools.fetch import fetch_webpage, search_web
 from smolagents import PythonInterpreterTool
 from tools.yttranscript import get_youtube_transcript, get_youtube_title_description
 from tools.stt import get_text_transcript_from_audio_file
 from tools.image import analyze_image
-from tools.mylogger import save_file_with_timestamp, mylog
+from common.mylogger import mylog
 import myprompts
 
 dotenv.load_dotenv()
@@ -43,8 +43,9 @@ openai_41mini_model = OpenAIServerModel(
 def check_final_answer(final_answer, agent_memory)  -> bool:
     """
     Check if the final answer is correct.
-    This is a placeholder function. You can implement your own logic here.
+    basic check on the length of the answer.
     """
+    mylog("check_final_answer", final_answer)
     # if return answer is more than 200 characters, we will assume it is not correct    
     if len(str(final_answer)) > 200:
         return False

tools/fetch.py

@@ -3,7 +3,7 @@ from smolagents import tool
 import requests
 from markdownify import markdownify as md
 from bs4 import BeautifulSoup
-from tools.mylogger import save_file_with_timestamp, mylog
+from common.mylogger import save_file_with_timestamp, mylog
 
 @tool
 def fetch_webpage(url: str, convert_to_markdown: bool = True) -> str: