Update app.py

app.py

@@ -3,76 +3,67 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from smolagents import CodeAgent, HfApiModel, DuckDuckGoSearchTool
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 # --- Basic Agent Definition ---
 class BasicAgent:
     def __init__(self):
-        # 1. Указываем ID модели. 
-        # Вы можете использовать "Qwen/Qwen3.6-27B" (или другую доступную модель Qwen 3.6),
-        # либо проверенную в курсе кодинг-модель "Qwen/Qwen2.5-Coder-32B-Instruct".
+        # Используем Qwen 2.5 Coder 32B как надежную и проверенную модель для агентов
         model_id = "Qwen/Qwen2.5-Coder-32B-Instruct"
-        
-        # Получаем токен из секретов Space, которые вы настроили ранее
         hf_token = os.getenv("HF_TOKEN")
+        
         if not hf_token:
-            print("⚠️ ВНИМАНИЕ: Секрет HF_TOKEN не найден в настройках Space!")
+            print("⚠️ WARNING: Секрет HF_TOKEN не обнаружен!")
 
-        # 2. Инициализируем модель
         self.model = HfApiModel(
             model_id=model_id,
             token=hf_token
         )
-        
-        # 3. Подключаем инструмент поиска в интернете
+        # Добавляем поиск DuckDuckGo
         self.search_tool = DuckDuckGoSearchTool()
         
-        # 4. Создаем CodeAgent, который умеет запускать Python-код для вычислений
+        # Создаем CodeAgent
         self.agent = CodeAgent(
             tools=[self.search_tool],
             model=self.model,
-            add_base_tools=True  # Подключает базовые инструменты smolagents
+            add_base_tools=True
         )
         print(f"BasicAgent успешно инициализирован с моделью: {model_id}")
 
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
         
-        # Системное указание агенту возвращать только результат (для Exact Match)
+        # Промпт для соблюдения Exact Match формата
         prompt = (
             f"{question}\n\n"
-            "ВАЖНО: Твой ответ будет проверяться автоматически на точное соответствие (Exact Match). "
-            "Выведи ТОЛЬКО финальный ответ (число, дату, слово или краткую фразу) "
-            "без каких-либо вводных слов, пояснений и оформления вроде 'The answer is:' или 'FINAL ANSWER'."
+            "ВАЖНОЕ ТРЕБОВАНИЕ: Твой ответ должен содержать только итоговое значение "
+            "(число, дату, слово или краткую фразу) без каких-либо вводных слов, пояснений "
+            "или разметки типа 'The final answer is:' или 'FINAL ANSWER'. "
+            "Выведи только чистый результат."
         )
         
         try:
-            # Запускаем агента для выполнения задачи
             result = self.agent.run(prompt)
-            # Приводим к строке и удаляем лишние пробелы по краям
-            fixed_answer = str(result).strip()
-            print(f"Agent returning answer: {fixed_answer}")
-            return fixed_answer
+            final_answer = str(result).strip()
+            print(f"Agent returning answer: {final_answer}")
+            return final_answer
         except Exception as e:
-            error_msg = f"ERROR: {str(e)}"
-            print(f"Ошибка при работе агента: {error_msg}")
-            return error_msg
+            print(f"Error during agent execution: {e}")
+            return f"ERROR: {e}"
+
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -82,13 +73,13 @@ 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)
+    # 1. Instantiate Agent
     try:
         agent = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+        
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
@@ -193,10 +184,6 @@ with gr.Blocks() as demo:
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
         """
     )
 
@@ -205,7 +192,6 @@ with gr.Blocks() as demo:
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -213,26 +199,14 @@ with gr.Blocks() as demo:
         outputs=[status_output, results_table]
     )
 
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+# --- Инициализация без блока 'if __name__ == "__main__":' ---
+space_host_startup = os.getenv("SPACE_HOST")
+space_id_startup = os.getenv("SPACE_ID")
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+if space_host_startup:
+    print(f"✅ SPACE_HOST found: {space_host_startup}")
+if space_id_startup:
+    print(f"✅ SPACE_ID found: {space_id_startup}")
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+print("Launching Gradio Interface for Basic Agent Evaluation...")
+demo.launch(debug=True, share=False)