app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from smolagents import CodeAgent, InferenceClientModel, DuckDuckGoSearchTool,Tool,tool,VisitWebpageTool,PythonInterpreterTool,FinalAnswerTool
import base64

# (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 ------

class BasicAgent:
    def __init__(self):
        print("Initializing Smolagent...")
        print("HF_TOKEN present:", bool(os.environ.get("HF_TOKEN")))

        @tool
        def fetch_task_file(task_id: str) -> str:
            """
            Downloads the file attached to a GAIA task and saves it locally.

            Args:
                task_id: The task_id of the current question.

            Returns:
                The local file path where the file was saved.
            """
            resp = requests.get(f"{DEFAULT_API_URL}/files/{task_id}")
            resp.raise_for_status()
            # Try to infer extension from content-type; default to .bin
            ext = resp.headers.get("content-type", "").split("/")[-1].split(";")[0]
            path = f"/tmp/{task_id}.{ext or 'bin'}"
            with open(path, "wb") as f:
                f.write(resp.content)
            return path
        @tool
        def get_youtube_transcript(url: str) -> str:
            """
            Fetches the transcript/captions of a YouTube video.

            Args:
                 url: The full YouTube video URL.

            Returns:
                The transcript text, or an error message if unavailable.
            """
            from youtube_transcript_api import YouTubeTranscriptApi
            import re

            match = re.search(r"(?:v=|youtu\.be/)([\w-]{11})", url)
            if not match:
                 return "Could not extract video ID from URL."
            video_id = match.group(1)

            try:
                transcript = YouTubeTranscriptApi().fetch(video_id)
                return " ".join(snippet.text for snippet in transcript)
            except Exception as e:
                return f"Transcript unavailable: {e}"
                
        @tool
        def fetch_webpage(url: str) -> str:
            """
            Fetches a webpage and returns its content as markdown. Use this instead of
            visit_webpage for Wikipedia and other sites that block requests without a
            real User-Agent header (visit_webpage will get a 403 on many of them).

            Args:
                url: The URL to fetch.

            Returns:
                The page content converted to markdown, or an error message.
            """
            from markdownify import markdownify
            import re

            headers = {
                "User-Agent": "GAIA-Agent-Research/1.0 (contact: kaindumushinge@arizona.edu) python-requests"
            }
            try:
                resp = requests.get(url, headers=headers, timeout=20)
                resp.raise_for_status()
                content = markdownify(resp.text).strip()
                content = re.sub(r"\n{3,}", "\n\n", content)
                return content[:40000]
            except Exception as e:
                return f"Error fetching the webpage: {e}"

        @tool
        def read_pdf_from_url(url: str) -> str:
            """
            Downloads a PDF from a URL (e.g. an arXiv paper) and extracts its text.
            Use this for PDFs reachable by URL; use read_pdf for local files already
            fetched via fetch_task_file.

            Args:
                url: Direct URL to a PDF file.

            Returns:
                The extracted text of the PDF, or an error message.
            """
            from pypdf import PdfReader
            import io

            headers = {
                "User-Agent": "GAIA-Agent-Research/1.0 (contact: kaindumushinge@arizona.edu) python-requests"
            }
            try:
                resp = requests.get(url, headers=headers, timeout=30)
                resp.raise_for_status()
                reader = PdfReader(io.BytesIO(resp.content))
                return "\n".join(page.extract_text() or "" for page in reader.pages)[:40000]
            except Exception as e:
                return f"Error fetching/parsing the PDF: {e}"

        @tool
        def read_spreadsheet(file_path: str) -> str:
            """
            Reads a CSV or Excel file and returns a text summary of its contents.

            Args:
                file_path: Local path to the spreadsheet file.

            Returns:
                A string representation of the dataframe.
            """
            if file_path.endswith(".csv"):
                df = pd.read_csv(file_path)
            else: 
                df = pd.read_excel(file_path)
            return df.to_string()

        class ReadPDFTool(Tool):
            name = "read_pdf"
            description = "Extracts text from a PDF file."
            inputs = {
                "file_path": {
                    "type": "string",
                    "description": "Local path to the PDF file.",
                }
            } 
            output_type = "string"

            def forward(self, file_path: str) -> str:
                from pypdf import PdfReader
                reader = PdfReader(file_path)
                return "\n".join(page.extract_text() or "" for page in reader.pages)

        class AnalyzeImageTool(Tool):
            name = "analyze_image"
            description = "Analyzes an image and answers a question about its contents."
            inputs = {
                    "file_path": {
                    "type": "string",
                    "description": "Local path to the image file.",
                     },
                    "question": {
                    "type": "string",
                    "description": "What to look for or answer about the image.",
                    },
                }
            output_type = "string"

            def forward(self, file_path: str, question: str) -> str:
                import base64
                from huggingface_hub import InferenceClient

                client = InferenceClient(token=os.environ["HF_TOKEN"])
                with open(file_path, "rb") as f:
                    image_bytes = f.read()
                image_b64 = base64.b64encode(image_bytes).decode("utf-8")

                result = client.chat_completion(
                    model="Qwen/Qwen2.5-VL-72B-Instruct",
                    messages=[
                        {
                            "role": "user",
                            "content": [
                                {"type": "text", "text": question},
                                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}},
                            ],
                        }
                    ],
                )
                return result.choices[0].message.content


        class TranscribeAudioTool(Tool):
            name = "transcribe_audio"
            description = "Transcribes speech from an audio file to text."
            inputs = {
                "file_path": {
                "type": "string",
                "description": "Local path to the audio file.",
            }
        }
            output_type = "string"

            def forward(self, file_path: str) -> str:
                from huggingface_hub import InferenceClient
                client = InferenceClient(token=os.environ["HF_TOKEN"])
                result = client.automatic_speech_recognition(
                    file_path,
                    model="openai/whisper-large-v3",
                )
                return result.text
        read_pdf = ReadPDFTool()
        analyze_image = AnalyzeImageTool()
        transcribe_audio = TranscribeAudioTool()
        self.agent = CodeAgent(
            tools=[
                DuckDuckGoSearchTool(),
                VisitWebpageTool(),
                fetch_webpage,
                read_pdf_from_url,
                PythonInterpreterTool(),
                FinalAnswerTool(),
                fetch_task_file,
                read_pdf,
                read_spreadsheet,
                get_youtube_transcript,
                analyze_image,
                transcribe_audio,
                ],
            model= InferenceClientModel(
                "Qwen/Qwen3-235B-A22B-Instruct-2507",
                provider="auto",
                temperature=0.3,
            ),
            additional_authorized_imports=["pandas", "requests", "re", "io"],
            instructions = ("You are an advanced CodeAgent that will show your capabilities to work in the real world by being tested in GAIA, the agent testing platform. If the question includes a task_id and mentions a file, call fetch_task_file first; route YouTube URLs to get_youtube_transcript, other URLs to fetch_webpage, PDFs at a URL to read_pdf_from_url, local PDFs to read_pdf, and spreadsheets to read_spreadsheet, using web_search only when no URL or file is given,then respond with only the exact final answer value, no explanation, no prefix."
                           "Always call fetch_webpage instead of visit_webpage: visit_webpage sends no User-Agent header and gets "
                           "blocked (403) by Wikipedia and many other sites; fetch_webpage sends a proper header and works reliably. "
                           "Only fall back to visit_webpage if fetch_webpage itself errors."
                           "When a fetched page contains a data table (e.g. Wikipedia infoboxes, Baseball-Reference stat tables), "
                           "prefer pandas.read_html(io.StringIO(page_text)) to extract it as a DataFrame instead of writing regex "
                           "against the raw markdown -- it is far more reliable. If a page's answer is already visible in the text "
                           "you fetched, read it directly rather than writing extraction code."
                           "Use the Thought Action observation to produce high quality results and only answer when you are sure you have performed the necessary steps for the task and question"
                           "If the file is an image, use analyze_image with a specific question about it"
                           "what to find. If the file is audio, use transcribe_audio first, then reason over the transcribed text "
                           "Use the PythonInterpreterTool for code interpretation in python"
                           "NEVER invent, guess, or simulate data you have not actually retrieved. If a "
                           "file cannot be fetched or a page cannot be read, say so explicitly rather "
                           "than fabricating plausible-looking data or answers. "
                           "The grader does an exact string match after light normalization, so format "
                           "the final answer exactly as the question asks: a bare number with no commas, "
                           "units, or currency symbols unless explicitly requested; as few words as "
                           "possible for a string answer, with no articles or explanatory text; and a "
                           "comma-separated list (no surrounding brackets) if multiple items are asked for."),
            max_steps=20,
        )
#answering questions 
    def __call__(self, question: str, task_id: str = None) -> str:
        print(f"Agent received question: {question[:50]}...")
        full_prompt = question
        if task_id:
            full_prompt = f"{question}\n\n(task_id for this question: {task_id})"
        try:
            # Pass the full prompt (including task_id) so the agent knows to fetch attached files
            answer = self.agent.run(full_prompt)
            return str(answer).strip()
        except Exception as e:
                import traceback
                traceback.print_exc()
                return "Error"

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 = 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")
        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)
    # 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)