Upload 3 files

agent.py

@@ -0,0 +1,510 @@
+"""GAIA Level-1 agent for the HF Agents Course final assignment.
+
+Built on smolagents CodeAgent. Works with ANY of these free LLM backends —
+set whichever API key you can get and the agent auto-detects it:
+
+    GROQ_API_KEY        console.groq.com/keys      (free, no card, fast)
+    CEREBRAS_API_KEY    cloud.cerebras.ai          (free tier)
+    OPENROUTER_API_KEY  openrouter.ai/keys         (has free models)
+    MISTRAL_API_KEY     console.mistral.ai         (free tier)
+    GOOGLE_API_KEY      aistudio.google.com/apikey (free, best multimodal)
+    HF_TOKEN            huggingface.co/settings/tokens (needs credits)
+
+Generic escape hatch for any other OpenAI-compatible endpoint:
+    OPENAI_API_KEY + OPENAI_BASE_URL + AGENT_MODEL
+
+Override the model with AGENT_MODEL if a default model id has been retired.
+"""
+
+import base64
+import mimetypes
+import os
+import re
+import tempfile
+import time
+
+import requests
+from smolagents import (
+    CodeAgent,
+    DuckDuckGoSearchTool,
+    VisitWebpageTool,
+    tool,
+)
+
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+WIKI_UA = "HF-Agents-Course-GAIA-Agent/1.0 (educational use)"
+
+# Provider registry: env var -> (base_url, default model, label)
+# Model ids are defaults only; override with AGENT_MODEL if one is retired.
+PROVIDERS = [
+    (
+        "GROQ_API_KEY",
+        "https://api.groq.com/openai/v1",
+        "llama-3.3-70b-versatile",
+        "Groq",
+    ),
+    (
+        "CEREBRAS_API_KEY",
+        "https://api.cerebras.ai/v1",
+        "llama-3.3-70b",
+        "Cerebras",
+    ),
+    (
+        "OPENROUTER_API_KEY",
+        "https://openrouter.ai/api/v1",
+        "meta-llama/llama-3.3-70b-instruct:free",
+        "OpenRouter",
+    ),
+    (
+        "MISTRAL_API_KEY",
+        "https://api.mistral.ai/v1",
+        "mistral-large-latest",
+        "Mistral",
+    ),
+    (
+        "GOOGLE_API_KEY",
+        "https://generativelanguage.googleapis.com/v1beta/openai/",
+        "gemini-2.5-flash",
+        "Gemini",
+    ),
+    (
+        "OPENAI_API_KEY",
+        os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
+        "gpt-4o-mini",
+        "OpenAI-compatible",
+    ),
+]
+
+
+def _active_provider():
+    """Return (api_key, base_url, model_id, label) for the first key found."""
+    for env_var, base_url, default_model, label in PROVIDERS:
+        key = os.getenv(env_var)
+        if key:
+            return key, base_url, os.getenv("AGENT_MODEL", default_model), label
+    return None, None, None, None
+
+
+API_KEY, BASE_URL, MODEL_ID, PROVIDER = _active_provider()
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY", "")
+
+
+# --------------------------------------------------------------------------
+# Gemini-only helper: native audio / video understanding
+# --------------------------------------------------------------------------
+def _gemini_generate(parts: list, retries: int = 3) -> str:
+    model = os.getenv("GEMINI_MODEL", "gemini-2.5-flash")
+    url = (
+        "https://generativelanguage.googleapis.com/v1beta/models/"
+        f"{model}:generateContent?key={GOOGLE_API_KEY}"
+    )
+    for attempt in range(retries):
+        resp = requests.post(url, json={"contents": [{"parts": parts}]}, timeout=180)
+        if resp.status_code == 429 and attempt < retries - 1:
+            time.sleep(20 * (attempt + 1))
+            continue
+        resp.raise_for_status()
+        return resp.json()["candidates"][0]["content"]["parts"][0]["text"]
+    return "ERROR: Gemini rate limited."
+
+
+def _inline_part(file_path: str) -> dict:
+    mime = mimetypes.guess_type(file_path)[0] or "application/octet-stream"
+    with open(file_path, "rb") as f:
+        return {
+            "inline_data": {
+                "mime_type": mime,
+                "data": base64.b64encode(f.read()).decode(),
+            }
+        }
+
+
+# --------------------------------------------------------------------------
+# Tools
+# --------------------------------------------------------------------------
+@tool
+def wikipedia_page(title: str) -> str:
+    """Fetch the full plain text of an English Wikipedia article. Use this
+    instead of visit_webpage for Wikipedia — it never gets blocked.
+
+    Args:
+        title: Article title, e.g. "Mercedes Sosa" or "1928 Summer Olympics".
+    """
+    try:
+        resp = requests.get(
+            "https://en.wikipedia.org/w/api.php",
+            params={
+                "action": "query",
+                "prop": "extracts",
+                "explaintext": 1,
+                "redirects": 1,
+                "format": "json",
+                "titles": title,
+            },
+            headers={"User-Agent": WIKI_UA},
+            timeout=45,
+        )
+        resp.raise_for_status()
+        pages = resp.json()["query"]["pages"]
+        page = list(pages.values())[0]
+        if "extract" not in page:
+            return f"No Wikipedia article found for '{title}'. Try wikipedia_search."
+        return page["extract"][:60000]
+    except Exception as e:
+        return f"ERROR fetching Wikipedia page: {e}"
+
+
+@tool
+def wikipedia_search(query: str) -> str:
+    """Search English Wikipedia and return matching article titles with snippets.
+    Use this to find the right title, then call wikipedia_page.
+
+    Args:
+        query: Search terms.
+    """
+    try:
+        resp = requests.get(
+            "https://en.wikipedia.org/w/api.php",
+            params={
+                "action": "query",
+                "list": "search",
+                "srsearch": query,
+                "srlimit": 10,
+                "format": "json",
+            },
+            headers={"User-Agent": WIKI_UA},
+            timeout=45,
+        )
+        resp.raise_for_status()
+        hits = resp.json()["query"]["search"]
+        return "\n".join(
+            f"- {h['title']}: {re.sub('<[^<]+?>', '', h['snippet'])}" for h in hits
+        ) or "No results."
+    except Exception as e:
+        return f"ERROR searching Wikipedia: {e}"
+
+
+@tool
+def fetch_url(url: str) -> str:
+    """Fetch a web page as text with a browser-like user agent. Use when
+    visit_webpage fails with a 403 Forbidden error.
+
+    Args:
+        url: The full URL to fetch.
+    """
+    try:
+        resp = requests.get(
+            url,
+            headers={
+                "User-Agent": (
+                    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
+                    "(KHTML, like Gecko) Chrome/122.0 Safari/537.36"
+                )
+            },
+            timeout=60,
+        )
+        resp.raise_for_status()
+        try:
+            from markdownify import markdownify
+
+            text = markdownify(resp.text)
+        except Exception:
+            text = resp.text
+        text = re.sub(r"\n{3,}", "\n\n", text)
+        return text[:50000]
+    except Exception as e:
+        return f"ERROR fetching url: {e}"
+
+
+@tool
+def transcribe_audio(file_path: str) -> str:
+    """Transcribe a local audio file (mp3/wav/m4a) to English text.
+
+    Args:
+        file_path: Absolute path to the local audio file to transcribe.
+    """
+    # Gemini: native audio understanding
+    if GOOGLE_API_KEY:
+        try:
+            return _gemini_generate(
+                [{"text": "Transcribe this audio verbatim."}, _inline_part(file_path)]
+            )
+        except Exception as e:
+            return f"ERROR transcribing audio: {e}"
+    # Groq hosts Whisper on an OpenAI-compatible endpoint
+    if os.getenv("GROQ_API_KEY"):
+        try:
+            with open(file_path, "rb") as f:
+                resp = requests.post(
+                    "https://api.groq.com/openai/v1/audio/transcriptions",
+                    headers={"Authorization": f"Bearer {os.getenv('GROQ_API_KEY')}"},
+                    files={"file": (os.path.basename(file_path), f)},
+                    data={"model": os.getenv("ASR_MODEL", "whisper-large-v3")},
+                    timeout=180,
+                )
+            resp.raise_for_status()
+            return resp.json()["text"]
+        except Exception as e:
+            return f"ERROR transcribing audio: {e}"
+    # HF Inference fallback
+    try:
+        from huggingface_hub import InferenceClient
+
+        result = InferenceClient(
+            token=os.getenv("HF_TOKEN")
+        ).automatic_speech_recognition(
+            file_path, model=os.getenv("ASR_MODEL", "openai/whisper-large-v3")
+        )
+        return result.text if hasattr(result, "text") else str(result)
+    except Exception as e:
+        return f"ERROR transcribing audio (no ASR backend available): {e}"
+
+
+@tool
+def analyze_image(file_path: str, question: str) -> str:
+    """Answer a question about a local image file using a vision model.
+
+    Args:
+        file_path: Absolute path to the local image file (png/jpg).
+        question: The question to answer about the image. Be specific; for
+            chess positions, ask for a full square-by-square board reading
+            AND the winning move, verified carefully.
+    """
+    if GOOGLE_API_KEY:
+        try:
+            return _gemini_generate([{"text": question}, _inline_part(file_path)])
+        except Exception as e:
+            return f"ERROR analyzing image: {e}"
+    if not API_KEY:
+        return "ERROR: no vision backend configured."
+    try:
+        mime = mimetypes.guess_type(file_path)[0] or "image/png"
+        with open(file_path, "rb") as f:
+            b64 = base64.b64encode(f.read()).decode()
+        vision_model = os.getenv("VISION_MODEL", MODEL_ID)
+        resp = requests.post(
+            BASE_URL.rstrip("/") + "/chat/completions",
+            headers={"Authorization": f"Bearer {API_KEY}"},
+            json={
+                "model": vision_model,
+                "max_tokens": 1500,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": question},
+                            {
+                                "type": "image_url",
+                                "image_url": {"url": f"data:{mime};base64,{b64}"},
+                            },
+                        ],
+                    }
+                ],
+            },
+            timeout=180,
+        )
+        resp.raise_for_status()
+        return resp.json()["choices"][0]["message"]["content"]
+    except Exception as e:
+        return (
+            f"ERROR analyzing image: {e}. The model may not support images; "
+            "set VISION_MODEL to a vision-capable model id."
+        )
+
+
+@tool
+def analyze_youtube_video(video_url: str, question: str) -> str:
+    """Watch a YouTube video and answer a question about its visual and audio
+    content (counting things on screen, quotes, scenes). Requires a Gemini key.
+
+    Args:
+        video_url: Full YouTube URL, e.g. https://www.youtube.com/watch?v=XXXX
+        question: The question to answer about the video.
+    """
+    if not GOOGLE_API_KEY:
+        return (
+            "ERROR: video analysis needs GOOGLE_API_KEY. Use "
+            "get_youtube_transcript or web_search for descriptions instead."
+        )
+    try:
+        return _gemini_generate(
+            [{"text": question}, {"file_data": {"file_uri": video_url}}]
+        )
+    except Exception as e:
+        return f"ERROR analyzing video: {e}"
+
+
+@tool
+def get_youtube_transcript(video_url: str) -> str:
+    """Fetch the transcript/captions of a YouTube video as plain text.
+
+    Args:
+        video_url: Full YouTube URL, e.g. https://www.youtube.com/watch?v=XXXX
+    """
+    try:
+        from youtube_transcript_api import YouTubeTranscriptApi
+
+        m = re.search(r"(?:v=|youtu\.be/)([\w-]{11})", video_url)
+        if not m:
+            return "ERROR: could not parse video id from URL."
+        vid = m.group(1)
+        try:
+            entries = [s.text for s in YouTubeTranscriptApi().fetch(vid)]
+        except AttributeError:
+            entries = [s["text"] for s in YouTubeTranscriptApi.get_transcript(vid)]
+        return " ".join(entries)[:20000]
+    except Exception as e:
+        return f"ERROR fetching transcript: {e}. Try web_search instead."
+
+
+@tool
+def read_file_as_text(file_path: str) -> str:
+    """Read a local text-like file (py, txt, csv, json, md) and return its content.
+
+    Args:
+        file_path: Absolute path to the local file.
+    """
+    try:
+        with open(file_path, "r", encoding="utf-8", errors="replace") as f:
+            return f.read()[:30000]
+    except Exception as e:
+        return f"ERROR reading file: {e}"
+
+
+# --------------------------------------------------------------------------
+# Answer-format guidance (GAIA is scored by EXACT MATCH)
+# --------------------------------------------------------------------------
+GAIA_INSTRUCTIONS = """You are a general AI assistant answering a benchmark
+question scored by EXACT string match. Work step by step with your tools,
+then call final_answer() with ONLY the answer itself.
+
+Formatting rules for the final answer (critical):
+- Do NOT write "FINAL ANSWER" or any prefix/suffix, explanation, or period
+  at the end. Output the bare answer only.
+- Numbers: plain digits, no thousands separators, no units ($, %, kg) unless
+  the question explicitly asks for them, no trailing ".0".
+- Strings: no articles ("the", "a"), no abbreviations unless asked.
+- Comma-separated lists: apply the rules above to each element, use ", "
+  (comma + space) between elements, and respect any ordering the question
+  asks for (e.g. alphabetical, ascending).
+- If asked for a first name / last name / city / country code only, return
+  exactly that and nothing more.
+
+Tool strategy:
+- Wikipedia questions: use wikipedia_search then wikipedia_page. Do NOT use
+  visit_webpage on wikipedia.org — it returns 403. The article text often
+  contains a discography or results table; read it carefully and count.
+- If visit_webpage returns 403 Forbidden, retry that URL with fetch_url.
+- Attached files: a local path is given; use read_file_as_text,
+  transcribe_audio, analyze_image, or pandas (pd.read_excel) for .xlsx.
+- For .xlsx, inspect the columns first, then compute. Format money like
+  89706.00 only when the question asks for two decimal places.
+- Python-code questions: read the code and reason through it carefully.
+- YouTube: try analyze_youtube_video, then get_youtube_transcript, then
+  web_search for third-party descriptions of the video.
+- Some questions are pure reasoning (reversed text, a group-theory table).
+  Solve those directly in python without searching.
+
+Reliability rules:
+- Never give up and guess a number you did not verify. If one source is
+  blocked, try another tool or another source.
+- Re-read the question's exact wording before answering (e.g. "included",
+  "as of July 2023", "IOC country code", "without abbreviations",
+  "first name only").
+"""
+
+
+class GAIAAgent:
+    """Wraps a smolagents CodeAgent with GAIA-specific tooling and prompting."""
+
+    def __init__(self):
+        if not API_KEY:
+            raise RuntimeError(
+                "No LLM API key found. Set one of: GROQ_API_KEY, "
+                "CEREBRAS_API_KEY, OPENROUTER_API_KEY, MISTRAL_API_KEY, "
+                "GOOGLE_API_KEY, or OPENAI_API_KEY (+OPENAI_BASE_URL)."
+            )
+        from smolagents import OpenAIServerModel
+
+        model = OpenAIServerModel(
+            model_id=MODEL_ID, api_base=BASE_URL, api_key=API_KEY
+        )
+        self.agent = CodeAgent(
+            model=model,
+            tools=[
+                DuckDuckGoSearchTool(),
+                VisitWebpageTool(),
+                fetch_url,
+                wikipedia_search,
+                wikipedia_page,
+                transcribe_audio,
+                analyze_image,
+                analyze_youtube_video,
+                get_youtube_transcript,
+                read_file_as_text,
+            ],
+            additional_authorized_imports=[
+                "pandas",
+                "numpy",
+                "openpyxl",
+                "json",
+                "csv",
+                "re",
+                "math",
+                "statistics",
+                "itertools",
+                "collections",
+                "datetime",
+            ],
+            max_steps=15,
+        )
+        print(f"GAIAAgent initialized (backend={PROVIDER}, model={MODEL_ID}).")
+
+    @staticmethod
+    def download_task_file(task_id: str, file_name: str) -> str | None:
+        """Download the file attached to a task; returns a local path or None."""
+        if not file_name:
+            return None
+        url = f"{DEFAULT_API_URL}/files/{task_id}"
+        for attempt in range(4):
+            try:
+                resp = requests.get(url, timeout=30)
+                resp.raise_for_status()
+                fd, path = tempfile.mkstemp(suffix=os.path.splitext(file_name)[1] or "")
+                with os.fdopen(fd, "wb") as f:
+                    f.write(resp.content)
+                return path
+            except Exception as e:
+                print(f"File download attempt {attempt + 1} failed for {task_id}: {e}")
+                time.sleep(3 * (attempt + 1))
+        return None
+
+    @staticmethod
+    def _clean(answer: str) -> str:
+        """Strip wrappers the model sometimes adds despite instructions."""
+        a = str(answer).strip()
+        a = re.sub(r"^(final answer\s*:?\s*)", "", a, flags=re.IGNORECASE)
+        a = a.strip().strip('"').strip("'").strip()
+        if a.endswith("."):
+            a = a[:-1]
+        return a
+
+    def __call__(self, question: str, task_id: str = "", file_name: str = "") -> str:
+        prompt = GAIA_INSTRUCTIONS + "\n\nQuestion: " + question
+        file_path = self.download_task_file(task_id, file_name)
+        if file_path:
+            prompt += (
+                f"\n\nAn attached file for this question was downloaded to the "
+                f"local path: {file_path} (original name: {file_name})."
+            )
+        elif file_name:
+            prompt += (
+                f"\n\nNOTE: this question references an attached file "
+                f"({file_name}) that could not be downloaded. Answer from "
+                f"other sources if possible."
+            )
+        try:
+            return self._clean(self.agent.run(prompt))
+        except Exception as e:
+            print(f"Agent error on task {task_id}: {e}")
+            return f"AGENT ERROR: {e}"

app.py

@@ -1,196 +1,189 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-
-# (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("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_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"
-    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)
-            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...")
+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Agent Definition ---
+from agent import GAIAAgent
+
+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 = GAIAAgent()
+    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_name = item.get("file_name", "")
+        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=task_id, file_name=file_name)
+            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

@@ -1,2 +1,10 @@
-gradio
-requests
+gradio[oauth]
+requests
+smolagents[toolkit]
+openai
+huggingface_hub
+pandas
+numpy
+openpyxl
+youtube-transcript-api
+markdownify