Update app.py

app.py

@@ -4,132 +4,173 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
+
 from smolagents import CodeAgent, DuckDuckGoSearchTool
 from smolagents.models import OpenAIServerModel
-import openai
 
-# --- Setup ---
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-if not OPENAI_API_KEY:
-    raise RuntimeError("Please set OPENAI_API_KEY in your Space secrets.")
-openai.api_key = OPENAI_API_KEY
+# System prompt as per your instructions
+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:
+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."""
 
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-OPENAI_MODEL_ID = os.getenv("OPENAI_MODEL_ID", "gpt-4o")
 
-model = OpenAIServerModel(model_id=OPENAI_MODEL_ID, api_key=OPENAI_API_KEY)
-search_tool = DuckDuckGoSearchTool()
-agent = CodeAgent(tools=[search_tool], model=model)
+class MyAgent:
+    def __init__(self):
+        # Initialize model with system prompt
+        self.model = OpenAIServerModel(
+            model_id="gpt-4",
+            system_message=SYSTEM_PROMPT
+        )
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool()],
+            model=self.model
+        )
 
-answer_formatting_prompt = """
-You are a smart assistant with access to tools like DuckDuckGoSearchTool(query: str). 
-Think step-by-step, then output your response.
+    def __call__(self, question: str) -> str:
+        # Run agent on the question
+        return self.agent.run(question)
 
-IMPORTANT: 
-FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers/strings. 
-Do NOT include commas, $ or % unless asked.
-Write digits plainly (e.g., '10', not 'ten').
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches questions, runs the agent, submits answers, returns status and results table.
+    """
+    space_id = os.getenv("SPACE_ID")
 
-Use format:
-FINAL ANSWER: <your_answer>
-"""
+    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
 
-def show_profile(profile):
-    if not profile:
-        return "⚠️ Not logged in."
-    return f"✅ Logged in as: {profile['username']}"
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
 
-def run_and_submit_all(login_info):
-    # login_info comes from LoginButton, it's None if not logged in
-    if not login_info:
-        return "⚠️ Please log in with your Hugging Face account.", pd.DataFrame()
+    try:
+        agent = MyAgent()
+    except Exception as e:
+        print(f"Error initializing agent: {e}")
+        return f"Error initializing agent: {e}", None
 
-    username = login_info["username"]
-    space_id = os.getenv("SPACE_ID", "")
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(f"Agent code URL: {agent_code}")
 
+    print(f"Fetching questions from: {questions_url}")
     try:
-        resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
-        resp.raise_for_status()
-        questions = resp.json()
+        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.")
     except Exception as e:
-        return f"❌ Error fetching questions: {e}", pd.DataFrame()
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
 
-    results, payload = [], []
-    for item in questions:
+    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 = item.get("question")
-        if not task_id or not question:
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            print(f"Skipping invalid item: {item}")
             continue
-        prompt = answer_formatting_prompt.strip() + f"\n\nQUESTION: {question.strip()}"
         try:
-            answer = agent.run(prompt)
+            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:
-            answer = f"AGENT ERROR: {e}"
-        results.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})
-        payload.append({"task_id": task_id, "submitted_answer": answer})
+            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}"})
 
-    if not payload:
-        return "⚠️ Agent returned no answers.", pd.DataFrame(results)
+    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)
 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        post = requests.post(
-            f"{DEFAULT_API_URL}/submit",
-            json={"username": username, "agent_code": agent_code, "answers": payload},
-            timeout=60
-        )
-        post.raise_for_status()
-        result = post.json()
-        score = result.get("score", "N/A")
-        correct = result.get("correct_count", "?")
-        attempted = result.get("total_attempted", "?")
-        message = result.get("message", "")
-        return (
-            f"✅ Submission Successful!\nUser: {username}\nScore: {score}% "
-            f"({correct}/{attempted})\nMessage: {message}",
-            pd.DataFrame(results)
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            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.')}"
         )
+        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)
+        return status_message, pd.DataFrame(results_log)
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        return status_message, pd.DataFrame(results_log)
     except Exception as e:
-        return f"❌ Submission failed: {e}", pd.DataFrame(results)
-
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        return status_message, pd.DataFrame(results_log)
 
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
-
-    login_button = gr.LoginButton()
-    login_status = gr.Textbox(label="Login Status")
-
+    gr.Markdown(
+        """
+        **Instructions:**
+        1. Clone this space, modify code to define your agent's logic, tools, and packages.
+        2. Log in to your Hugging Face account using the button below.
+        3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see your score.
+        
+        **Note:** Submitting can take some time.
+        """
+    )
+
+    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)
 
-    # Show login status when user logs in
-    login_button.click(fn=show_profile, inputs=[login_button], outputs=[login_status])
-
-    # Run evaluation on click, pass login_button's state as input
-    run_button.click(fn=run_and_submit_all, inputs=[login_button], outputs=[status_output, results_table])
-
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
-    demo.launch()
-
-
-#import gradio as gr
-
-#def show_profile(profile):
- #   if not profile:
-   #     return "⚠️ Not logged in."
-  #  return f"✅ Logged in as: {profile['username']}"
-
- # with gr.Blocks() as demo:
-   #   gr.Markdown("## 🔐 Hugging Face OAuth Login")
-
-   #   login_button = gr.LoginButton()
-   #   output = gr.Textbox(label="Login Status")
-
-    #  login_button.click(fn=show_profile, inputs=[login_button], outputs=[output])
-
- # demo.launch()
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    space_host = os.getenv("SPACE_HOST")
+    space_id = os.getenv("SPACE_ID")
+
+    if space_host:
+        print(f"✅ SPACE_HOST found: {space_host}")
+        print(f"   Runtime URL should be: https://{space_host}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id:
+        print(f"✅ SPACE_ID found: {space_id}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?).")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)