Upload 3 files

agent.py

@@ -6,7 +6,9 @@ from __future__ import annotations
 
 import asyncio
 import os
-from typing import Any, Sequence, Callable, List
+import time
+import datetime
+from typing import Any, Sequence, Callable, List, Optional
 
 from dotenv import load_dotenv
 from agents import Agent, Runner, FunctionTool, Tool
@@ -21,6 +23,12 @@ from tools import (
     duckduckgo_search,
 )
 
+# ---------------------------------------------------------------------------
+# Logging Utility
+# ---------------------------------------------------------------------------
+def log(msg):
+    print(f"[{datetime.datetime.now():%Y-%m-%d %H:%M:%S}] {msg}")
+
 # ---------------------------------------------------------------------------
 # Load the added system prompt
 # ---------------------------------------------------------------------------
@@ -82,18 +90,32 @@ class GAIAAgent:
 
     def __init__(self, *, extra_tools: Sequence[FunctionTool] | None = None):
         self._agent = _build_agent(extra_tools=extra_tools)
+        # Store the model id for logging
+        self.model_id = _select_model()
 
-    async def _arun(self, question: str) -> str:
-        result = await Runner.run(self._agent, question)
+    async def _arun(self, question: str, q_index: Optional[int] = None) -> str:
+        q_num = q_index + 1 if q_index is not None else "?"
+        log(f"Answering question {q_num}:")
+        log(f"    Question: {question!r}")
+        log(f"    Model: {self.model_id}")
+
+        t0 = time.time()
+        try:
+            result = await Runner.run(self._agent, question)
+            duration = time.time() - t0
+            log(f"    Total duration: {duration:.2f} seconds.")
+        except Exception as e:
+            log(f"    Error during answer: {e}")
+            raise
         return str(result.final_output).strip()
 
-    def __call__(self, question: str, **kwargs: Any) -> str:
+    def __call__(self, question: str, q_index: Optional[int] = None, **kwargs: Any) -> str:
         try:
             loop = asyncio.get_running_loop()
         except RuntimeError:
-            return asyncio.run(self._arun(question))
+            return asyncio.run(self._arun(question, q_index=q_index))
         else:
-            return loop.run_until_complete(self._arun(question))
+            return loop.run_until_complete(self._arun(question, q_index=q_index))
 
 
 def gaia_agent(*, extra_tools: Sequence[FunctionTool] | None = None) -> GAIAAgent:

app.py

@@ -2,30 +2,34 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
+import datetime
 
 # --- Our Agent ---
 from agent import gaia_agent
 
+# Logging utility
+def log(msg):
+    print(f"[{datetime.datetime.now():%Y-%m-%d %H:%M:%S}] {msg}")
+
 # Debugging level. If DEBUG=0 then DEBUG will be False. If DEBUG=1 then DEBUG will be True.
 DEBUG = os.getenv("DEBUG", "0") == "1"
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the 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")  # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+        username = f"{profile.username}"
+        log(f"User logged in: {username}")
     else:
-        print("User not logged in.")
+        log("User not logged in.")
         return "Please Login to Hugging Face with the button.", None
 
     api_url = DEFAULT_API_URL
@@ -35,16 +39,16 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     # 1. Instantiate Agent (now using OpenAI Agents SDK)
     try:
         agent = gaia_agent()
-        print("OpenAI Agent instantiated successfully.")
+        log("OpenAI Agent instantiated successfully.")
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
+        log(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 ( useful for others so please keep it public)
+
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+    log(agent_code)
 
     # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+    log(f"Fetching questions from: {questions_url}")
     import json
 
     try:
@@ -52,57 +56,55 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-            print("Fetched questions list is empty.")
+            log("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
+        log(f"Fetched {len(questions_data)} GAIA questions.")
     except json.JSONDecodeError as e:
-        print(f"Error decoding JSON response from questions endpoint: {e}")
-        print(f"Response text: {response.text[:500]}")
+        log(f"Error decoding JSON response from questions endpoint: {e}")
+        log(f"Response text: {response.text[:500]}")
         return f"Error decoding server response for questions: {e}", None
     except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
+        log(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
+        log(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
     # 3. Run the Agent
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+    log(f"Running agent on {len(questions_data)} questions...")
+    for idx, item in enumerate(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}")
+            log(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
-            # --- DEBUG LOGGING ---
+            submitted_answer = agent(question_text, q_index=idx)
             if DEBUG:
-                print(f"[DEBUG] Task {task_id}: Answer type: {type(submitted_answer)}, Value: {repr(submitted_answer)}")
+                log(f"[DEBUG] Task {task_id}: Answer type: {type(submitted_answer)}, Value: {repr(submitted_answer)}")
             else:
-                print(f"[{task_id}] {question_text[:50]}... → {submitted_answer[:40]}")
+                log(f"[{task_id}] {question_text[:50]}... → {submitted_answer[:40]}")
 
-            # Force string type here just in case (defensive)
             submitted_answer = str(submitted_answer).strip()
             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}"})
+            log(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:
-        print("Agent did not produce any answers to submit.")
+        log("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission 
+    # 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)
+    log(status_update)
 
     # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    log(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
@@ -114,7 +116,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             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.")
+        log("Submission successful.")
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
     except requests.exceptions.HTTPError as e:
@@ -125,22 +127,22 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         except requests.exceptions.JSONDecodeError:
             error_detail += f" Response: {e.response.text[:500]}"
         status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
+        log(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)
+        log(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)
+        log(status_message)
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
     except Exception as e:
         status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
+        log(status_message)
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
@@ -178,7 +180,7 @@ with gr.Blocks() as demo:
 if __name__ == "__main__":
     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")  # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -186,14 +188,14 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    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("-" * (60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Agent Evaluation…")
     demo.launch(debug=True, share=False)

tools.py

@@ -7,12 +7,31 @@ from __future__ import annotations
 import contextlib
 import io
 import os
+import time
+import datetime
 from typing import List, Dict
 
 from agents import function_tool
 
+def log(msg):
+    print(f"[{datetime.datetime.now():%Y-%m-%d %H:%M:%S}] {msg}")
+
+def log_tool_call(func):
+    def wrapper(*args, **kwargs):
+        t0 = time.time()
+        log(f"Step: {func.__name__} started.")
+        try:
+            result = func(*args, **kwargs)
+            log(f"Step: {func.__name__} completed in {time.time() - t0:.2f}s.")
+            return result
+        except Exception as e:
+            log(f"Step: {func.__name__} error: {e}")
+            raise
+    return wrapper
+
 # 1. --------------------------------------------------------------------
 @function_tool
+@log_tool_call
 def python_run(code: str) -> str:
     """Execute trusted Python code and return the captured stdout together with
     the repr() of the last expression (or `_result` variable if set).
@@ -36,6 +55,7 @@ def python_run(code: str) -> str:
 
 # 2. --------------------------------------------------------------------
 @function_tool
+@log_tool_call
 def load_spreadsheet(path: str, sheet: str | int | None = None) -> list[Dict[str, str]]:
     """Read .csv, .xls or .xlsx from disk and return rows as list of dictionaries.
 
@@ -65,6 +85,7 @@ def load_spreadsheet(path: str, sheet: str | int | None = None) -> list[Dict[str
 
 # 3. --------------------------------------------------------------------
 @function_tool
+@log_tool_call
 def youtube_transcript(url: str, lang: str = "en") -> str:
     """Fetch the subtitles of a YouTube video.
 
@@ -84,6 +105,7 @@ def youtube_transcript(url: str, lang: str = "en") -> str:
 
 # 4. --------------------------------------------------------------------
 @function_tool
+@log_tool_call
 def transcribe_audio(path: str, model: str = "whisper-1") -> str:
     """Transcribe an audio file using OpenAI Whisper.
 
@@ -104,6 +126,7 @@ def transcribe_audio(path: str, model: str = "whisper-1") -> str:
 
 # 5. --------------------------------------------------------------------
 @function_tool
+@log_tool_call
 def image_ocr(path: str) -> str:
     """Perform OCR on an image using Tesseract.
 
@@ -120,6 +143,7 @@ def image_ocr(path: str) -> str:
 
 # 6. --------------------------------------------------------------------
 @function_tool
+@log_tool_call
 def duckduckgo_search(query: str, max_results: int = 5) -> List[Dict[str, str]]:
     """Search DuckDuckGo and return a list of result dicts with title, href and body.
 
@@ -139,4 +163,4 @@ def duckduckgo_search(query: str, max_results: int = 5) -> List[Dict[str, str]]:
                     "body": r.get("body", ""),
                 }
             )
-    return results
+    return results