Update app.py

app.py

@@ -3,36 +3,21 @@ import gradio as gr
 import requests
 import pandas as pd
 
-# ---- Constants ----
+from smolagents.agents import ToolCallingAgent
+
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# ---- Smolagents imports ----
-from smolagents import WikipediaSearchTool, DuckDuckGoSearchTool
-
-# ---- Agent Definition ----
-class SmolAgent:
-    def __init__(self):
-        self.wiki = WikipediaSearchTool()
-        self.duck = DuckDuckGoSearchTool()
-    def __call__(self, question: str) -> str:
-        # Try Wikipedia first, fall back to DuckDuckGo if nothing useful.
-        wiki_result = ""
-        duck_result = ""
-        try:
-            wiki_result = self.wiki.run(question)
-        except Exception as e:
-            wiki_result = f"(Wiki error: {e})"
-        # If Wikipedia yields nothing or too short, use DuckDuckGo as fallback
-        if wiki_result and len(str(wiki_result)) > 40:
-            return str(wiki_result)
-        try:
-            duck_result = self.duck.run(question)
-        except Exception as e:
-            duck_result = f"(DuckDuckGo error: {e})"
-        if duck_result:
-            return str(duck_result)
-        # If both fail, return error info
-        return f"Wikipedia: {wiki_result}\nDuckDuckGo: {duck_result}\n(No answer found.)"
+def create_agent():
+    """
+    Create a multi-tool agent with SmolAgents.
+    """
+    # You specify the tool names as strings!
+    agent = ToolCallingAgent(
+        tools=["wikipedia", "duckduckgo", "web_search"],
+        model="huggingface",
+        model_kwargs={"repo_id": "HuggingFaceH4/zephyr-7b-beta"},
+    )
+    return agent
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
@@ -47,23 +32,31 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
+    # Create agent with all relevant tools
     try:
-        agent = SmolAgent()
+        agent = create_agent()
     except Exception as e:
         return f"Error initializing agent: {e}", None
+
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
+    # Fetch Questions
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
+            print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
-    except Exception as e:
+        print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
         return f"Error fetching questions: {e}", None
+    except Exception as e:
+        return f"An unexpected error occurred fetching questions: {e}", None
 
+    # Run the agent on all questions
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
@@ -71,21 +64,28 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
             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})
         except Exception as e:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"(Agent error: {e})"})
 
     if not answers_payload:
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    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)
 
+    # Submit answers
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
@@ -100,45 +100,52 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
     except Exception as e:
+        status_message = f"Submission Failed: {e}"
         results_df = pd.DataFrame(results_log)
-        return f"Submission Failed: {e}", results_df
+        return status_message, results_df
 
-# --- Build Gradio Interface using Blocks ---
+# --- Gradio UI ---
 with gr.Blocks() as demo:
     gr.Markdown("# SmolAgent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
-        1. Clone and modify this space to improve your agent logic as you see fit.
-        2. Log in to your Hugging Face account with the button below.
-        3. Click 'Run Evaluation & Submit All Answers' to begin.
-        ---
-        **Disclaimer:** Submission may take a while depending on the number of questions and agent speed.
+        
+        - Clone and modify this space to improve your agent logic as you see fit.
+        - Log in to your Hugging Face account with the button below.
+        - Click 'Run Evaluation & Submit All Answers' to begin.
+
+        Disclaimer: Submission may take a while depending on the number of questions and agent speed.
         """
     )
-
     gr.LoginButton()
     run_button = gr.Button("Run Evaluation & Submit All Answers")
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
     run_button.click(
         fn=run_and_submit_all,
         outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    print("\n--- App Starting ---\n")
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
     space_host_startup = os.getenv("SPACE_HOST")
     space_id_startup = os.getenv("SPACE_ID")
+
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL: https://{space_host_startup}.hf.space")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
     else:
         print("ℹ️  SPACE_HOST not found (running locally?)")
+
     if space_id_startup:
         print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+        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 not found")
-    print("--- App Starting ---\n")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+    print("Launching Gradio Interface for SmolAgent Evaluation...")
     demo.launch(debug=True, share=False)