Update app.py

app.py

@@ -1,196 +1,194 @@
 import os
+import logging
+
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+import openai
+from openai import OpenAI
+
+from smolagents import CodeAgent, DuckDuckGoSearchTool, tool
+from smolagents.models import OpenAIServerModel
+
+# --- Logging ---
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+logger = logging.getLogger(__name__)
 
-# (Keep Constants as is)
 # --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+DEFAULT_API_URL   = "https://agents-course-unit4-scoring.hf.space"
+OPENAI_MODEL_ID   = os.getenv("OPENAI_MODEL_ID", "gpt-4.1")
+OPENAI_API_KEY    = os.getenv("OPENAI_API_KEY")
+if not OPENAI_API_KEY:
+    raise RuntimeError("Please set OPENAI_API_KEY in your Space secrets.")
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+# --- Configure OpenAI SDK (for tools if needed) ---
+openai.api_key = "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"
+client = OpenAI()
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+# --- Tools ---
+
+@tool
+def summarize_query(query: str) -> str:
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Reframes an unclear search query to improve relevance.
+
+    Args:
+        query (str): The original search query.
+
+    Returns:
+        str: A concise, improved version.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    return f"Summarize and reframe: {query}"
 
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+@tool
+def wikipedia_search(page: str) -> str:
+    """
+    Fetches the summary extract of an English Wikipedia page.
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+    Args:
+        page (str): e.g. 'Mercedes_Sosa_discography'
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    Returns:
+        str: The page’s extract text.
+    """
     try:
-        agent = BasicAgent()
+        url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{page}"
+        r = requests.get(url, timeout=10)
+        r.raise_for_status()
+        return r.json().get("extract", "")
     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)
+        logger.exception("Wikipedia lookup failed")
+        return f"Wikipedia error: {e}"
+
+search_tool    = DuckDuckGoSearchTool()
+wiki_tool      = wikipedia_search
+summarize_tool = summarize_query
+
+# --- ReACT Prompt ---
+
+instruction_prompt = """
+You are a ReACT agent with three tools: 
+ • DuckDuckGoSearchTool(query: str)
+ • wikipedia_search(page: str)
+ • summarize_query(query: str)
+
+Internally, for each question:
+1. Thought: decide which tool to call.
+2. Action: call the chosen tool.
+3. Observation: record the result.
+4. If empty/irrelevant:
+   Thought: retry with summarize_query + DuckDuckGoSearchTool.
+   Record new Observation.
+5. Thought: integrate observations.
+
+Finally, output 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.
+
+"""
+
+# --- Build the Agent with OpenAIServerModel ---
+
+model = OpenAIServerModel(
+    model_id=OPENAI_MODEL_ID,
+    api_key=OPENAI_API_KEY
+)
+
+smart_agent = CodeAgent(
+    tools=[search_tool, wiki_tool, summarize_tool],
+    model=model
+)
+
+# --- Gradio Wrapper ---
+
+class BasicAgent:
+    def __init__(self):
+        logger.info("Initialized SmolAgent with OpenAI GPT-4.1")
+
+    def __call__(self, question: str) -> str:
+        if not question.strip():
+            return "AGENT ERROR: empty question"
+        prompt = instruction_prompt.strip() + "\n\nQUESTION: " + question.strip()
+        try:
+            return smart_agent.run(prompt)
+        except Exception as e:
+            logger.exception("Agent run error")
+            return f"AGENT ERROR: {e}"
+
+# --- Submission Logic ---
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    if not profile:
+        return "Please log in to Hugging Face.", None
+
+    username   = profile.username
+    space_id   = os.getenv("SPACE_ID", "")
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+    agent      = BasicAgent()
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+    # fetch
     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.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
+        resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
+        resp.raise_for_status()
+        questions = resp.json() or []
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    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}")
-            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:
-             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}"})
+        logger.exception("Failed fetch")
+        return f"Error fetching questions: {e}", None
 
-    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)
+    logs, payload = [], []
+    for item in questions:
+        tid = item.get("task_id")
+        q   = item.get("question")
+        if not tid or not q:
+            continue
+        ans = agent(q)
+        logs.append({"Task ID": tid, "Question": q, "Submitted Answer": ans})
+        payload.append({"task_id": tid, "submitted_answer": ans})
 
-    # 4. Prepare Submission 
-    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)
+    if not payload:
+        return "Agent did not produce any answers.", pd.DataFrame(logs)
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    # submit
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
+        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()
+        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.')}"
+            f"User: {result.get('username')}\n"
+            f"Score: {result.get('score','N/A')}%\n"
+            f"({result.get('correct_count','?')}/"
+            f"{result.get('total_attempted','?')})\n"
+            f"Message: {result.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 requests.exceptions.JSONDecodeError:
-            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 status, pd.DataFrame(logs)
     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
+        logger.exception("Submit failed")
+        return f"Submission Failed: {e}", pd.DataFrame(logs)
 
+# --- Gradio App ---
 
-# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        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.
-        """
-    )
-
+    gr.Markdown("# SmolAgent GAIA Runner 🚀")
+    gr.Markdown("""
+**Instructions:**  
+1. Clone this space.  
+2. In Settings → Secrets, add `OPENAI_API_KEY` and (optionally) `OPENAI_MODEL_ID`.  
+3. Log in to Hugging Face.  
+4. Click **Run Evaluation & Submit All Answers**.
+""")
     gr.LoginButton()
-
-    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(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    btn = gr.Button("Run Evaluation & Submit All Answers")
+    out_status = gr.Textbox(label="Status", lines=5, interactive=False)
+    out_table  = gr.DataFrame(label="Questions & Answers", wrap=True)
+    btn.click(run_and_submit_all, outputs=[out_status, out_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.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)