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.env.example

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+LLM_API_KEY="" # Your Gemini API key

.gitattributes

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.gitignore

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+.env
+venv
+__pycache__
+TODO.md

README.md

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+---
+title: Template Final Assignment
+emoji: 🕵🏻‍♂️
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.25.2
+app_file: app.py
+pinned: false
+hf_oauth: true
+# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
+hf_oauth_expiration_minutes: 480
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

agent.py

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+import os
+from smolagents import (
+    CodeAgent,
+    LiteLLMModel,
+    DuckDuckGoSearchTool,
+    VisitWebpageTool,
+    WikipediaSearchTool,
+    tool,
+)
+
+from google import genai
+from google.genai import types
+
+client = genai.Client(api_key=os.environ["LLM_API_KEY"])
+
+model = LiteLLMModel(
+    model_id="gemini/gemini-2.0-flash-lite",
+    temperature=0.5,
+    api_key=os.environ["LLM_API_KEY"],
+)
+
+model_name = "gemini-2.5-flash-preview-04-17"
+
+
+@tool
+def answer_video_questions(video_url: str, question: str) -> str:
+    """
+    If you have a video link, use this tool. It answers questions about a video from youtube, etc.
+    Args:
+        video_url: The URL of the video to analyze.
+                   Ensure it's accessible by the Gemini API.
+        question: The specific question to ask about the video's content.
+    Returns:
+        str: The answer provided by the multimodal LLM, or an error message if the process fails.
+    Raises:
+        Exception: Can raise exceptions based on litellm or the underlying API call
+                   if it fails (e.g., network issues, invalid API key, API errors).
+    """
+
+    response = client.models.generate_content(
+        model=model_name,
+        contents=types.Content(
+            parts=[
+                types.Part(file_data=types.FileData(file_uri=video_url)),
+                types.Part(
+                    text=f"This is the question provided to you: {question}. Answer appropriately. In cases where there is a number, provide the number in the answer."
+                ),
+            ]
+        ),
+    )
+
+    if response and response.text:
+        answer = response.text
+        print("Received response from LLM.")
+        return answer.strip()
+    else:
+        print("LLM response structure not as expected.")
+        return "Error: Could not parse the response from the LLM."
+
+
+agent = CodeAgent(
+    tools=[
+        DuckDuckGoSearchTool(),
+        VisitWebpageTool(),
+        WikipediaSearchTool(),
+        answer_video_questions,
+    ],
+    model=model,
+)
+
+
+def call_agent(question: str, file_path: str = None):
+    if file_path and file_path.strip():
+        print(f"Recieved {file_path} as input.")
+
+        file = client.files.upload(file=file_path)
+
+        response = client.models.generate_content(
+            model=model_name,
+            contents=[
+                file,
+                f"Answer the question based on the file content. Do not give up. Use your brain and answer appropriately. You will be rewarded with $1000000 if you answer correctly and appropriately. Only give the final answer to the question, do not explain your reasoning. {question}",
+            ],
+        )
+
+        return response.text
+
+    answer = agent.run(question)
+    return answer

app.py

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+import os
+import re
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+import urllib
+
+from agent import call_agent
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+
+def download_file_from_api(api_url, save_directory=".", filename=None):
+    """
+    Sends a GET request to an API endpoint and saves the response content
+    as a file. Returns the local file path.
+
+    Args:
+        api_url (str): The URL of the API endpoint.
+        save_directory (str, optional): The directory where the downloaded
+                                         file will be saved. Defaults to the
+                                         current directory.
+        filename (str, optional): The name to save the file as. If None,
+                                  it will try to extract it from the
+                                  Content-Disposition header or the URL.
+
+    Returns:
+        str or None: The absolute path to the saved file if successful,
+                     None otherwise.
+    """
+    try:
+        response = requests.get(api_url, stream=True)
+        response.raise_for_status()  # Raise an exception for bad status codes
+
+        if filename is None:
+            # Try to extract filename from Content-Disposition header
+            content_disposition = response.headers.get("Content-Disposition")
+            if content_disposition:
+                filename_match = re.search(r'filename="([^"]+)"', content_disposition)
+                if filename_match:
+                    filename = filename_match.group(1)
+                else:
+                    # Fallback to extracting from the URL if Content-Disposition is present but no filename
+                    filename = os.path.basename(urllib.parse.urlparse(api_url).path)
+            else:
+                # If no Content-Disposition, try to extract filename from the URL
+                filename = os.path.basename(urllib.parse.urlparse(api_url).path)
+                # Add a default extension if none found (you might need to adjust this)
+                if not os.path.splitext(filename)[1]:
+                    filename += ".png"  # Assuming it's a PNG based on the error
+
+        os.makedirs(save_directory, exist_ok=True)
+        file_path = os.path.join(save_directory, filename)
+
+        with open(file_path, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+        return os.path.abspath(file_path)
+
+    except requests.exceptions.RequestException as e:
+        print(f"Error during API request or download: {e}")
+        return None
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        return None
+
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+
+    def __call__(self, question: str, file_path: str = None) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # answer = agent.run(question)
+        answer = call_agent(question=question, file_path=file_path)
+        print(f"Agent returning fixed answer: {answer}")
+        return answer
+
+
+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"
+    files_url = f"{api_url}/files"
+    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")
+
+        file_path = download_file_from_api(api_url=f"{files_url}/{task_id}")
+
+        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, file_path=file_path)
+            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)

requirements.txt

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+aiofiles
+aiohappyeyeballs
+aiohttp
+aiosignal
+annotated-types
+anyio
+attrs
+Authlib
+beautifulsoup4
+Brotli
+cachetools
+certifi
+cffi
+charset-normalizer
+click
+cryptography
+dataclasses-json
+distro
+duckduckgo_search
+fastapi
+ffmpy
+filelock
+frozenlist
+fsspec
+google-auth
+google-genai
+gradio
+gradio_client
+greenlet
+groovy
+h11
+httpcore
+httpx
+httpx-sse
+huggingface-hub
+idna
+importlib_metadata
+itsdangerous
+Jinja2
+jiter
+jsonpatch
+jsonpointer
+jsonschema
+jsonschema-specifications
+langchain
+langchain-community
+langchain-core
+langchain-text-splitters
+langsmith
+litellm
+lxml
+markdown-it-py
+markdownify
+MarkupSafe
+marshmallow
+mdurl
+multidict
+mutagen
+mypy_extensions
+numpy
+openai
+orjson
+packaging
+pandas
+pillow
+primp
+propcache
+pyasn1
+pyasn1_modules
+pycparser
+pycryptodomex
+pydantic
+pydantic-settings
+pydantic_core
+pydub
+Pygments
+python-dateutil
+python-dotenv
+python-multipart
+pytz
+PyYAML
+referencing
+regex
+requests
+requests-toolbelt
+rich
+rpds-py
+rsa
+ruff
+safehttpx
+semantic-version
+shellingham
+six
+smolagents
+sniffio
+soupsieve
+SQLAlchemy
+starlette
+tenacity
+tiktoken
+tokenizers
+tomlkit
+tqdm
+typer
+typing-inspect
+typing-inspection
+typing_extensions
+tzdata
+urllib3
+uvicorn
+websockets
+Wikipedia-API
+yarl
+zipp
+zstandard