Update app.py

app.py

@@ -2,202 +2,111 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-import openai
-from smolagents import CodeAgent
-from smolagents import DuckDuckGoSearchTool
+from smolagents import CodeAgent, DuckDuckGoSearchTool
+from smolagents.models import OpenAIServerModel
 
-
-# --- Constants ---
+# Constants
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- SmolCodeAgent Definition with system prompt and DuckDuckGo tool ---
-class SmolCodeAgent:
-    SYSTEM_PROMPT = """
-You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: 
+# === Define the Smol Agent ===
+class MyAgent:
+    def __init__(self):
+        self.model = OpenAIServerModel()  # Assumes OpenAI-compatible API via env vars
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool()],
+            model=self.model,
+            system_message="""You are a general AI assistant. I will ask you a question.
+Report your thoughts, and finish your answer with the following template:
 FINAL ANSWER: [YOUR FINAL ANSWER].
 YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list 
-of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, 
-apply the above rules depending of whether the element to be put in the list is a number or a string.
-"""
-
-    def __init__(self, api_key=None, model="gpt-4o"):
-        if api_key is None:
-            api_key = os.getenv("OPENAI_API_KEY")
-        if not api_key:
-            raise RuntimeError("OPENAI_API_KEY not set in environment.")
-        openai.api_key = api_key
-        self.model = model
-
-        # Add DuckDuckGoSearchTool to tools
-        self.tools = [DuckDuckGoSearchTool()]
-        self.agent = CodeAgent(model=self.model, tools=self.tools)
+of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""
+        )
 
     def __call__(self, question: str) -> str:
-        print(f"SmolCodeAgent received question (first 50 chars): {question[:50]}...")
-        try:
-            full_prompt = self.SYSTEM_PROMPT.strip() + "\n\nQuestion: " + question
-            answer = self.agent.run(full_prompt)
-            print(f"SmolCodeAgent answer (first 100 chars): {answer[:100]}...")
-            return answer
-        except Exception as e:
-            error_msg = f"AGENT ERROR: {e}"
-            print(error_msg)
-            return error_msg
+        return self.agent.run(question)
 
-# --- Main logic to run the agent on all questions and submit ---
+# === Submission Logic ===
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
-
     if profile:
         username = profile.username
         print(f"User logged in: {username}")
     else:
-        print("User not logged in.")
         return "Please Login to Hugging Face with the button.", None
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    submit_url = f"{DEFAULT_API_URL}/submit"
 
-    # Instantiate your SmolCodeAgent here
     try:
-        agent = SmolCodeAgent()
+        agent = MyAgent()
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
 
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(f"Agent code URL: {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
-        print(f"Fetched {len(questions_data)} questions.")
+        res = requests.get(questions_url, timeout=15)
+        res.raise_for_status()
+        questions_data = res.json()
     except Exception as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
+        return f"Failed to fetch questions: {e}", None
 
-    # Run the agent on each question
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+
     for item in questions_data:
         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}")
+        question = item.get("question")
+        if not task_id or question is None:
             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})
+            answer = agent(question)
+            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})
+            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
         except Exception as e:
-            print(f"Error running agent on task {task_id}: {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, "Submitted Answer": f"ERROR: {e}"})
 
     if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+        return "No answers generated.", pd.DataFrame(results_log)
 
-    # Prepare submission data
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    print(f"Submitting {len(answers_payload)} answers for user '{username}'...")
+    submission_data = {
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
 
-    # Submit answers
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
+        res = requests.post(submit_url, json=submission_data, timeout=60)
+        res.raise_for_status()
+        result_data = res.json()
+        summary = (
             f"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
+            f"Score: {result_data.get('score', '?')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')})\n"
+            f"Message: {result_data.get('message', '')}"
         )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except Exception:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        return summary, pd.DataFrame(results_log)
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
+        return f"Submission failed: {e}", pd.DataFrame(results_log)
 
-# --- Gradio UI ---
+# === Gradio UI ===
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner with SmolCodeAgent")
-    gr.Markdown(
-        """
-        **Instructions:**
-        1. Clone this space, modify the agent logic and tools.
-        2. Log in to your Hugging Face account using the button below.
-        3. Click 'Run Evaluation & Submit All Answers' to run the agent on all questions and submit.
-        ---
-        **Note:**
-        Submissions may take time while the agent processes questions.
-        """
-    )
-
-    login_btn = gr.LoginButton()
+    gr.Markdown("# Agent Evaluation Runner (SmolAgents)")
+    gr.Markdown("""
+**Instructions:**
+1. Clone this space and customize your agent.
+2. Log in with Hugging Face.
+3. Click 'Run Evaluation' to answer and submit.
+""")
+
+    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)
+    status_output = gr.Textbox(label="Status", lines=4, interactive=False)
+    results_table = gr.DataFrame(label="Results", wrap=True)
 
-    # We need to pass the login profile to the function
-    run_button.click(
-        fn=run_and_submit_all,
-        inputs=[login_btn],
-        outputs=[status_output, results_table]
-    )
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
-    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 should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable 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}")
-        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.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    print("Launching...")
+    demo.launch(debug=True)