app.py

import base64
import os
import requests
import subprocess
import inspect

import gradio as gr
import pandas as pd

import basic_agent

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# Tracing to Lanfuse
from opentelemetry.sdk.trace import TracerProvider
from openinference.instrumentation.smolagents import SmolagentsInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

LANGFUSE_PUBLIC_KEY = os.environ.get("LANGFUSE_PUBLIC_KEY")
LANGFUSE_SECRET_KEY = os.environ.get("LANGFUSE_SECRET_KEY")

LANGFUSE_AUTH = base64.b64encode(
    f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()

os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = os.environ.get(
    "LANGFUSE_HOST") + "/api/public/otel"
os.environ[
    "OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"

# Create a TracerProvider for OpenTelemetry
trace_provider = TracerProvider()

# Add a SimpleSpanProcessor with the OTLPSpanExporter to send traces
trace_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter()))

# Set the global default tracer provider
from opentelemetry import trace

trace.set_tracer_provider(trace_provider)
tracer = trace.get_tracer(__name__)

# Instrument smolagents with the configured provider
SmolagentsInstrumentor().instrument(tracer_provider=trace_provider)


def get_git_hash():
    try:
        process = subprocess.run(['git', 'rev-parse', 'HEAD'], capture_output=True, text=True, check=True)
        git_hash = process.stdout.strip()
        return git_hash
    except subprocess.CalledProcessError as e:
        print(f"Error: {e}")
        return None

def run_and_submit_all(profile: gr.OAuthProfile | None):
  """
    Fetches all questions, runs the basic_agent.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

  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

  git_hash = get_git_hash()
  print("-"*23 + f"> {git_hash=} <" + "-"*23)

  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)
  try:
    agent = basic_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(agent_code)

  # 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
    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

  # 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")
    filename = item.get("file_name")
    if filename:
      file_url = f"{api_url}/files/{task_id}"
      print(f"Question {task_id=} has attachment: {file_url=}")
      quest_with_url = f"{question_text}\nFile url: {file_url}"
    else:
      quest_with_url = question_text

    if not task_id or question_text is None:
      print(f"Skipping item with missing task_id or question: {item}")
      continue
    try:
      print(f"calling agent")
      submitted_answer = agent(quest_with_url)
      print(f"called agent: {submitted_answer=}")
      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}"
      })

  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)

  # 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)

  # 5. Submit
  print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
  try:
    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 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:
    status_message = f"An unexpected error occurred during submission: {e}"
    print(status_message)
    results_df = pd.DataFrame(results_log)
    return status_message, results_df


# --- 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.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])

if __name__ == "__main__":
  print("\n" + "-" * 30 + " NEW 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")

  git_hash = get_git_hash()
  print("*"*23 + f"  {git_hash=}  " + "*"*23)
  print("-" * 60 + "\n")

  print("Launching Gradio Interface for Basic Agent Evaluation...")
  demo.launch(debug=True, share=False)