Add smolagents GAIA agent

app.py

@@ -1,196 +1,288 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
-# (Keep Constants as is)
+from smolagents import (
+    CodeAgent,
+    DuckDuckGoSearchTool,
+    InferenceClientModel,
+    tool,
+)
+
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- 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):
+# --- Custom Tools ---
+
+@tool
+def download_file_from_task(task_id: str) -> str:
+    """
+    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:
+    """
+    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.
+
+    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 BasicAgent on them, submits all answers,
-    and displays the results.
+    Fetches all questions, runs the agent on them, submits answers, and displays results.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID")
+
+    if not profile:
+        return "Please login to Hugging Face first.", None
 
-    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
+    username = profile.username
+    print(f"User logged in: {username}")
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Build agent
     try:
-        agent = BasicAgent()
+        agent = build_agent()
     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(agent_code)
+    print(f"Agent code link: {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:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
+            return "Fetched questions list is empty.", 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
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+        return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # 3. Run 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}")
             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(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})
+            submitted_answer = agent.run(augmented_question)
+            submitted_answer = str(submitted_answer).strip()
+            print(f"Task {task_id}: {submitted_answer[:80]}")
         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}"})
+            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,
+        })
 
     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 "Agent produced no answers.", pd.DataFrame(results_log)
 
-    # 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)
+    # 4. Submit
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload,
+    }
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    print(f"Submitting {len(answers_payload)} answers...")
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response = requests.post(submit_url, json=submission_data, timeout=120)
         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"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
+        return final_status, pd.DataFrame(results_log)
     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
+            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)
     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)
 
 
-# --- Build Gradio Interface using Blocks ---
+# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# GAIA Agent - Unit 4 Final Assignment")
     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.
 
-        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.
+        The agent uses web search, file downloading, and Python calculations to answer questions.
+        Target: >= 30% to earn the course certificate.
         """
     )
 
     gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
 
-    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)
+    status_output = gr.Textbox(
+        label="Submission Result",
+        lines=6,
+        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]
+        outputs=[status_output, results_table],
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
+    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
+
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+    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 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(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("Launching Gradio Interface for Basic Agent Evaluation...")
+    print("-" * 74 + "\n")
+    print("Launching Gradio Interface...")
     demo.launch(debug=True, share=False)

requirements.txt

@@ -1,2 +1,5 @@
+smolagents[toolkit]
 gradio
-requests
+requests
+pandas
+duckduckgo-search