Add smolagents GAIA agent

app.py

@@ -1,289 +1,196 @@
 import os
 import gradio as gr
 import requests
+import inspect
 import pandas as pd
 
-from smolagents import (
-    CodeAgent,
-    DuckDuckGoSearchTool,
-    InferenceClientModel,
-    tool,
-)
-
-
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Custom Tools ---
-
-@tool
-def download_file_from_task(task_id: str) -> str:
+# --- 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
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Downloads a file associated with a GAIA task and returns its content as text.
-    Use this when a question references an attached file.
-
-    Args:
-        task_id: The task ID string of the GAIA question.
-    """
-    url = f"{DEFAULT_API_URL}/files/{task_id}"
-    try:
-        response = requests.get(url, timeout=30)
-        response.raise_for_status()
-        content_type = response.headers.get("content-type", "")
-
-        # Try to decode as text
-        try:
-            text = response.content.decode("utf-8")
-            # Truncate if too long
-            if len(text) > 8000:
-                text = text[:8000] + "\n[... truncated ...]"
-            return text
-        except UnicodeDecodeError:
-            return f"File downloaded but contains binary content (content-type: {content_type}). Size: {len(response.content)} bytes."
-
-    except requests.exceptions.RequestException as e:
-        return f"Error downloading file for task {task_id}: {e}"
-
-
-@tool
-def python_calculator(code: str) -> str:
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
     """
-    Executes a Python expression or small snippet and returns the result.
-    Use for arithmetic, unit conversions, date calculations, or any numerical reasoning.
-    Only use safe, simple expressions. No imports needed for basic math.
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
-    Args:
-        code: A Python expression or short snippet to evaluate (e.g. '2 ** 10', 'round(3.14159 * 2, 4)')
-    """
-    import math
-    import datetime
-    allowed_globals = {
-        "__builtins__": {},
-        "math": math,
-        "datetime": datetime,
-        "abs": abs,
-        "round": round,
-        "int": int,
-        "float": float,
-        "str": str,
-        "len": len,
-        "sum": sum,
-        "min": min,
-        "max": max,
-        "sorted": sorted,
-        "range": range,
-        "list": list,
-        "dict": dict,
-        "set": set,
-        "zip": zip,
-        "enumerate": enumerate,
-        "print": print,
-    }
-    try:
-        result = eval(code, allowed_globals)
-        return str(result)
-    except Exception:
-        try:
-            exec_globals = allowed_globals.copy()
-            exec(code, exec_globals)
-            output = exec_globals.get("result", exec_globals.get("output", "Code executed but no 'result' variable found."))
-            return str(output)
-        except Exception as e:
-            return f"Error executing code: {e}"
-
-
-# --- System Prompt ---
-SYSTEM_PROMPT = """You are an expert research assistant tasked with answering questions from the GAIA benchmark.
-
-CRITICAL RULES:
-1. Your final answer must be EXACT and CONCISE. No explanations, no sentences, no punctuation unless part of the answer.
-2. If the answer is a number, return ONLY the number (e.g. "42" not "The answer is 42").
-3. If the answer is a name, return ONLY the name (e.g. "Marie Curie" not "The answer is Marie Curie").
-4. If the answer is a list, return items separated by commas (e.g. "cat, dog, fish").
-5. If a question references a file or attachment, use the download_file_from_task tool with the task_id.
-6. Always search the web for factual questions before answering.
-7. Double-check calculations using the python_calculator tool.
-8. Never include "FINAL ANSWER:" in your response.
-9. Match the exact format requested in the question (abbreviation, full name, number, etc.).
-"""
-
-
-# --- Agent Factory ---
-def build_agent():
-    model = InferenceClientModel(
-        model_id="Qwen/Qwen2.5-72B-Instruct",
-        max_tokens=2048,
-        temperature=0.1,
-    )
-
-    agent = CodeAgent(
-        model=model,
-        tools=[
-            DuckDuckGoSearchTool(),
-            download_file_from_task,
-            python_calculator,
-        ],
-        max_steps=8,
-        verbosity_level=1,
-        additional_authorized_imports=["math", "datetime", "re", "json", "csv", "io"],
-    )
-
-    # Inject system prompt into agent
-    agent.system_prompt = SYSTEM_PROMPT + "\n\n" + agent.system_prompt
-
-    return agent
-
-
-# --- Main Runner ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the agent on them, submits answers, and displays results.
-    """
-    space_id = os.getenv("SPACE_ID")
-
-    if not profile:
-        return "Please login to Hugging Face first.", None
-
-    username = profile.username
-    print(f"User logged in: {username}")
+    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
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Build agent
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = build_agent()
+        agent = BasicAgent()
     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)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(f"Agent code link: {agent_code}")
+    print(agent_code)
 
-    # 2. Fetch questions
+    # 2. 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:
-            return "Fetched questions list is empty.", None
+             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:
+    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
+    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 agent on each question
+    # 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
-
-        # Inject task_id into question so agent can use download_file_from_task
-        augmented_question = f"[task_id: {task_id}]\n\n{question_text}"
-
         try:
-            submitted_answer = agent.run(augmented_question)
-            submitted_answer = str(submitted_answer).strip()
-            print(f"Task {task_id}: {submitted_answer[:80]}")
+            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:
-            submitted_answer = f"AGENT ERROR: {e}"
-            print(f"Error on task {task_id}: {e}")
-
-        answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-        results_log.append({
-            "Task ID": task_id,
-            "Question": question_text[:120],
-            "Submitted Answer": submitted_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 answers_payload:
-        return "Agent produced no answers.", pd.DataFrame(results_log)
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Submit
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload,
-    }
+    # 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)
 
-    print(f"Submitting {len(answers_payload)} answers...")
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=120)
+        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"Score: {result_data.get('score', 'N/A')}% "
+            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.")
-        return final_status, pd.DataFrame(results_log)
+        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:
-            detail = e.response.json().get("detail", e.response.text)
-        except Exception:
-            detail = e.response.text[:500]
-        return f"Submission Failed: {detail}", pd.DataFrame(results_log)
+            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
     except Exception as e:
-        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
 
 
-# --- Gradio Interface ---
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent - Unit 4 Final Assignment")
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
-        1. Log in with your Hugging Face account below.
-        2. Click **Run Evaluation & Submit** to start the agent on all 20 GAIA questions.
-        3. Results and score will appear below.
 
-        The agent uses web search, file downloading, and Python calculations to answer questions.
-        Target: >= 30% to earn the course certificate.
+        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.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    status_output = gr.Textbox(
-        label="Submission Result",
-        lines=6,
-        interactive=False,
-    )
-    results_table = gr.DataFrame(
-        label="Questions and Agent Answers",
-        wrap=True,
-    )
+    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],
+        outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
-
+    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: {space_host_startup}")
-    if space_id_startup:
-        print(f"SPACE_ID: {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")
+        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("-" * 74 + "\n")
-    print("Launching Gradio Interface...")
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)