app.py

import os
import re
import gradio as gr
import requests
import inspect
import pandas as pd
from io import BytesIO

from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool, OpenAIServerModel

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


# --- Basic Agent Definition ---
class BasicAgent:
    """
    Agent logic:
    - CodeAgent (not ToolCallingAgent) because chained multi-step reasoning
      (search -> extract -> compute -> format) survives code execution far
      better than JSON tool-call chains, which fail silently on malformed
      structured output.
    - DuckDuckGoSearchTool + VisitWebpageTool cover the web-lookup questions
      (e.g. "Mercedes Sosa albums 2000-2009").
    - _fetch_file pulls any attached file for a task from {api_url}/files/{task_id}
      and hands the raw bytes into the CodeAgent's execution namespace via
      additional_args, so generated code can parse it (pandas/openpyxl etc.)
      if the question needs it.
    - _format_answer is a safety net, not the primary defense. The prompt
      instructs exact-match-aware output; this regex cleanup just strips
      stray quotes/periods/whitespace that slip through.
    - Known gap: questions that require actual video frame analysis (e.g. the
      YouTube bird-count question) are NOT solved by this tool set. Search
      can find a video's title/description but not analyze its frames. That
      would need a separate vision-capable step - deliberately out of scope
      for this baseline.
    """

    def __init__(self):
        print("BasicAgent initialized.")
        groq_key = os.getenv("GROQ_API_KEY")
        if not groq_key:
            raise ValueError(
                "GROQ_API_KEY not found in environment. "
                "Add it as a Space secret (Settings > Variables and secrets). "
                "Get a free key at https://console.groq.com"
            )
        self.model = OpenAIServerModel(
            model_id="llama-3.3-70b-versatile",
            api_base="https://api.groq.com/openai/v1",
            api_key=groq_key,
        )
        self.agent = CodeAgent(
            tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
            model=self.model,
        )

    def _fetch_file(self, task_id: str, api_url: str = DEFAULT_API_URL):
        """Fetch any file attached to this task. Returns raw bytes or None."""
        if not task_id:
            return None
        try:
            resp = requests.get(f"{api_url}/files/{task_id}", timeout=15)
            if resp.status_code == 404:
                # No file attached to this task - not an error.
                return None
            resp.raise_for_status()
            return resp.content
        except requests.exceptions.RequestException as e:
            print(f"File fetch failed for task {task_id}: {e}")
            return None

    def _format_answer(self, raw: str) -> str:
        """Strip surrounding quotes/whitespace/trailing punctuation - safety net only."""
        answer = str(raw).strip()
        answer = re.sub(r'^["\']|["\']$', '', answer).strip()
        if answer.endswith('.') and not answer.replace('.', '').isdigit():
            answer = answer.rstrip('.')
        return answer.strip()

    def __call__(self, question: str, task_id: str = None) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            file_bytes = self._fetch_file(task_id)

            prompt = question
            additional_args = {}
            if file_bytes is not None:
                additional_args["file_bytes"] = file_bytes
                prompt += (
                    "\n\n(An attached file for this task is available in your "
                    "execution namespace as the variable `file_bytes` (raw bytes). "
                    "Use pandas/openpyxl/BytesIO as needed to parse it if it's "
                    "relevant to answering the question.)"
                )

            result = self.agent.run(prompt, additional_args=additional_args or None)
            answer = self._format_answer(result)
            print(f"Agent returning answer: {answer}")
            return answer
        except Exception as e:
            print(f"Agent error on task {task_id}: {e}")
            return f"AGENT_ERROR: {e}"


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    space_id = os.getenv("SPACE_ID")

    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. Instantiate Agent
    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    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")
        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, task_id)
            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)
    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 + " App Starting " + "-" * 30)
    space_host_startup = os.getenv("SPACE_HOST")
    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(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)