update codes

app.py

@@ -3,9 +3,13 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+
 import random
-from smolagents import CodeAgent, InferenceClientModel, OpenAIModel, DuckDuckGoSearchTool
+from smolagents import CodeAgent, InferenceClientModel, TransformersModel, OpenAIModel
+from smolagents import DuckDuckGoSearchTool
+
 
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
@@ -13,43 +17,32 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        deepseek_key = os.getenv("DEEPSEEK_API_KEY")
-        hf_token = os.getenv("HF_TOKEN")
+        # Initialize the Hugging Face model
+        # hf_token = os.getenv("HF_TOKEN")
+
+        # model = InferenceClientModel(
+        #     token=hf_token
+        # )
+
+        model = OpenAIModel(
+            model_id="deepseek-chat",
+            api_base="https://api.deepseek.com",
+            api_key=os.getenv("DEEPSEEK_API_KEY"),
+        )
         
-        # 고성능 추론엔진인 DeepSeek API 우선 바인딩, 키 누락 시 무료 인퍼런스 서버리스로 폴백 조치
-        if deepseek_key:
-            model = OpenAIModel(
-                model_id="deepseek-chat",
-                api_base="https://api.deepseek.com",
-                api_key=deepseek_key,
-            )
-            print("BasicAgent: Initialized with DeepSeek API Engine.")
-        else:
-            model = InferenceClientModel(
-                model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-                token=hf_token
-            )
-            print("BasicAgent: DeepSeek Key not found. Falling back to Hugging Face Inference API.")
-                
+        # Initialize the web search tool
         search_tool = DuckDuckGoSearchTool()
-                
-        # 35점 베이스라인의 다단계 계획 수립(Planning Loop) 및 기본 인터프리터 툴 에코시스템 이식
+        
+        # Create Alfred with all the tools
         self.alfred = CodeAgent(
             tools=[search_tool], 
             model=model,
-            add_base_tools=True,  
-            planning_interval=3   
+            add_base_tools=True,  # Add any additional base tools
+            planning_interval=3   # Enable planning every 3 steps
         )
 
     def __call__(self, question: str) -> str:
-        try:
-            result = self.alfred.run(question)
-            if result is None:
-                return "unknown"
-            return str(result).strip()
-        except Exception as e:
-            print(f"Error during agent runtime execution: {e}")
-            return "unknown"
+        return self.alfred.run(question)
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -171,6 +164,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
+
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
@@ -180,6 +174,10 @@ 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.
         """
     )
 
@@ -188,6 +186,7 @@ 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(
@@ -197,8 +196,9 @@ with gr.Blocks() as demo:
 
 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")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -206,7 +206,7 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup:
+    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")