app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from smolagents import CodeAgent, InferenceClientModel, tool
from smolagents import ActionStep, PlanningStep, ToolCall, Tool
from smolagents import (
    CodeAgent,
    DuckDuckGoSearchTool,
    VisitWebpageTool,
    WikipediaSearchTool,
    OpenAIServerModel,
    SpeechToTextTool,
    FinalAnswerTool,
)
import yaml, importlib, requests, json, os, base64, re
import wikipediaapi
from typing import List, Dict, Any, Union, Optional
from openai import OpenAI

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

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
model = OpenAIServerModel(
                model_id="gpt-4.1-mini",
                api_key=os.getenv('OPENAI_API_KEY'),
        )
# PROMPTS
system_prompt = """You are a general AI assistant. I will ask you a question.
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
If your answer is a number and you are not explicitly asked for a string, write it in numerals instead of words, and don't use comma to write your number nor use units such as $ or percent sign unless specified otherwise.
If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise.
If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.

Answer questions as literally as you can, making as few assumptions as possible. Restrict the answer to the narrowest definition that still satifies the question.
If you are provied with a video, please watch and summarize the entire video before answering the question. The correct answer may be present only in a few frames of the video.
If you are asked to prove something, first state your assumptions and think step by step before giving your final answer.
"""
req_instruction = (
    "You are a highly capable and autonomous agent named {{name}}, designed to solve complex tasks efficiently.\n"
    "A valued client has assigned you the following task:\n"
    "---\n"
    "Task:\n"
    "{{task}}\n"
    "---\n"
    "To complete this task successfully, follow these steps carefully:\n"
    "    1. Comprehend the task and identify the intended goal.\n"
    "    2. Break the task into clear, logical steps.\n"
    "    3. Select and prepare the tools or resources you need.\n"
    "    - If a tool does not return useful results on the first attempt, consider retrying it with a simpler, more general, or slightly modified input.\n"
    "       Avoid switching to a different tool too quickly unless clearly necessary.\n"
    "    4. Set up the required environment or context.\n"
    "    5. Execute each step methodically.\n"
    "    6. Monitor outcomes and identify any deviations.\n"
    "    7. Revise your plan if necessary based on feedback.\n"
    "    8. Maintain internal state and track progress.\n"
    "    9. Verify that the goal has been fully achieved.\n"
    "   10. Present the final result clearly and concisely.\n"
    "If you succeed, you will be rewarded with a significant bonus.\n\n"
    "Your final_answer MUST be:\n"
    "- a number (retain its original type; do not include units),\n"
    "- a concise phrase,\n"
    "- or a comma-separated list of numbers or strings, with a space after each comma (e.g., \"1, 2, 3\", not \"1,2,3\"; do not include articles or abbreviations).\n\n"
    "Only the content passed to the final_answer tool will be preserved. Any other content will be discarded."
)

prompts = yaml.safe_load(
    importlib.resources.files("smolagents.prompts").joinpath("code_agent.yaml").read_text()
)
prompts['managed_agent']['task'] = req_instruction
prompts['managed_agent']['report'] = "{{final_answer}}"

# Tools
@tool
def wikipedia_df_tool(query: str) -> List[pd.DataFrame]:
    """
    Use this tool first for Wikipedia searches, before switching to text-based tools, and retry this tool if no results are found.

    Retrieve useful tabular data from English Wikipedia.

    This tool searches for HTML tables on the Wikipedia page matching the given query,
    and returns them as a list of Pandas DataFrames. It always returns a list — if no tables
    are found, the list will be empty.

    If no results are found, retry this tool with a more general, simpler, or alternative version of the query.
    Examples of simplifications: removing terms like 'discography', using only the person's name, or trying keywords like 'albums', 'list', or 'table'.


    Args:
        query: A Wikipedia page title or related phrase (e.g., "Argentina", "Mercedes Sosa discography").
    """
    wiki = wikipediaapi.Wikipedia(user_agent='MyProjectName (merlin@example.com)', language='en')
    wiki_page = wiki.page(query)
    try:
      url = wiki_page.fullurl
    except Exception:
      return []
    dfs = pd.read_html(url)
    return dfs if dfs else []

@tool
def get_file_from_task(task_id: str, file_name: str) -> str:
  """
  Use this tool to download the file content associated with the given task_id if exists.
  Returns absolute file path.
  Args:
        task_id: The unique identifier of the task whose associated file should be downloaded. 
        This is used to locate the file on the server via the API endpoint.
        file_name: The desired name (or path) to save the downloaded file locally. 
        This will be the name of the file written to disk.
  Returns:
        The absolute path to the downloaded file saved on the local filesystem.
  """
  response = requests.get(f"{DEFAULT_API_URL}/files/{task_id}", timeout=15)
  response.raise_for_status()
  with open(file_name, 'wb') as file:
    file.write(response.content)
  return os.path.abspath(file_name)

@tool
def load_text_file(file_path: str) -> str:
  """
    Reads and returns the content of a UTF-8 encoded text file.

    Args:
        file_path (str): Path to the file to be read.

    Returns:
        str: The content of the file as a string.
    """
  with open(file_path, 'r', encoding='utf-8') as file:
    return file.read()



@tool
def analyze_image(image_path: str, task: str) -> str:
    """
    Analyzes the image at the given path using OpenAI's vision model,
    based on the provided task description.

    Args:
        image_path (str): Path to the image file.
        task (str): Task to perform on the image (e.g., describe, interpret, extract data).

    Returns:
        str: Result of the analysis as a string.
    """
    client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

    with open(image_path, "rb") as f:
        encoded_image = base64.b64encode(f.read()).decode("utf-8")

    

    prompt = (
        "You are an expert image analysis tool. Please examine the following image and perform the task:\n\n"
        f"{task}"
    )

    response =  client.responses.create(
        model="gpt-4.1-mini",
        input=[
            {"role": "user", "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"data:image/jpeg;base64,{encoded_image}"}
            ]}
        ]
    )

    return response.output_text

def summarize_steps(agent):
  summary = []
  for step in agent.memory.steps:
    if isinstance(step, ActionStep):
      args = step.tool_calls[0].arguments.strip().replace('\n', ' ')
      summary.append({'Step': step.step_number, 'Summary': args})
  return summary

mp3_to_text_tool = Tool.from_space(
    "mrfakename/fast-whisper-turbo",
    name="voice_to_text",
    description="Transcribes an English audio file into text. Returns the transcribed text.",
    api_name="/transcribe"
)

my_agent = CodeAgent(
        tools=[
            DuckDuckGoSearchTool(),
            VisitWebpageTool(),
            WikipediaSearchTool(),
            mp3_to_text_tool,
            FinalAnswerTool(),
            wikipedia_df_tool,
            get_file_from_task,
            load_text_file,
            analyze_image,
        ],
        model=model,
        prompt_templates=prompts,
        additional_authorized_imports = ["pandas", "requests", "BeautifulSoap"],
        name="tikito",
    )

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

    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 ( modify this part to create your agent)
    try:
        agent = my_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)

    # 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:
            # Add task id and filename to assist the agent in the task
            question_text += "\n\nTask ID: " + str(task_id) + "\n"
            filename = item.get("file_name")
            if filename:
                question_text += f"Filename (use 'get_file_from_task' to retrieve tthe file): {filename}"
            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:
             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 + " 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")

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