Upload app.py

app.py

@@ -1,196 +1,410 @@
-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 re
+import json
+import time
+import requests
+import gradio as gr
+import pandas as pd
+import wikipediaapi
+from pypdf import PdfReader
+from youtube_transcript_api import YouTubeTranscriptApi
+from smolagents import (
+    CodeAgent,
+    InferenceClientModel,
+    LiteLLMModel,
+    WebSearchTool,
+    VisitWebpageTool,
+    tool,
+)
+
+# --- 1. API Constants & Cache ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+CACHE_FILE = "cached_answers.json"
+
+
+# --- 2. Advanced Multimodal & Research Tools for GAIA ---
+
+@tool
+def search_wikipedia(query: str) -> str:
+    """
+    Fetches the full summary and sections of a Wikipedia page for an exact entity or topic.
+    Use this whenever a question mentions Wikipedia, historical facts, discographies, or biographies.
+    Args:
+        query: Exact title of the Wikipedia article to look up (e.g., "Mercedes Sosa", "Eliud Kipchoge").
+    """
+    try:
+        wiki = wikipediaapi.Wikipedia(user_agent="GAIA_Agent/1.0", language="en")
+        page = wiki.page(query)
+        if not page.exists():
+            return f"No Wikipedia page found for '{query}'. Try searching via web_search first to get the exact title."
+        return f"Title: {page.title}\nURL: {page.fullurl}\nContent:\n{page.text[:18000]}"
+    except Exception as e:
+        return f"Wikipedia error: {e}"
+
+
+@tool
+def get_youtube_transcript(youtube_url_or_id: str) -> str:
+    """
+    Extracts the full text transcript/captions of a YouTube video.
+    Use this whenever a question includes a youtube.com or youtu.be link!
+    Args:
+        youtube_url_or_id: The full YouTube URL or the 11-character video ID.
+    """
+    try:
+        match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11}).*", youtube_url_or_id)
+        video_id = match.group(1) if match else youtube_url_or_id.strip()
+        ytt_api = YouTubeTranscriptApi()
+        fetched = ytt_api.fetch(video_id)
+        lines = [f"[{int(snippet.start)}s] {snippet.text}" for snippet in fetched]
+        return "\n".join(lines)[:18000]
+    except Exception as e:
+        return f"Error fetching YouTube transcript: {e}"
+
+
+@tool
+def read_pdf_file(file_path: str) -> str:
+    """
+    Extracts all text from a local PDF (.pdf) file.
+    Args:
+        file_path: Path to the local PDF file.
+    """
+    try:
+        reader = PdfReader(file_path)
+        text = []
+        for idx, page in enumerate(reader.pages):
+            text.append(f"--- Page {idx + 1} ---\n{page.extract_text()}")
+        return "\n".join(text)[:20000]
+    except Exception as e:
+        return f"Error reading PDF: {e}"
+
+
+@tool
+def read_text_or_code_file(file_path: str) -> str:
+    """
+    Reads the text content of a local file (.txt, .py, .csv, .md, .json, .pdb).
+    Args:
+        file_path: Path to the local file downloaded for the task.
+    """
+    try:
+        with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
+            return f.read()[:20000]
+    except Exception as e:
+        return f"Error reading file: {e}"
+
+
+@tool
+def read_excel_or_csv(file_path: str) -> str:
+    """
+    Reads an Excel (.xlsx, .xls) or CSV (.csv) file and returns its columns, summary stats, and full table data.
+    Args:
+        file_path: Path to the local Excel or CSV file.
+    """
+    try:
+        if file_path.endswith(".csv"):
+            df = pd.read_csv(file_path)
+        else:
+            df = pd.read_excel(file_path)
+        return (
+            f"Columns: {list(df.columns)}\n"
+            f"Shape: {df.shape}\n"
+            f"Data:\n{df.to_string()}\n"
+            f"Numeric Summary:\n{df.describe().to_string()}"
+        )
+    except Exception as e:
+        return f"Error reading spreadsheet: {e}"
+
+
+@tool
+def inspect_image_or_audio(file_path: str, task_instruction: str) -> str:
+    """
+    Uses a multimodal model to transcribe audio (.mp3, .wav) or analyze images (.png, .jpg, .webp) such as chessboards, charts, or handwriting.
+    Args:
+        file_path: Path to the local audio or image file.
+        task_instruction: Specific question or instruction about the image or audio (e.g., "Transcribe this audio word for word" or "List the exact coordinates of all pieces on this chessboard").
+    """
+    try:
+        # Route 1: Gemini Multimodal API (Handles both Audio and Images natively)
+        if os.getenv("GEMINI_API_KEY"):
+            from google import genai
+            client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
+            uploaded_file = client.files.upload(file=file_path)
+            response = client.models.generate_content(
+                model="gemini-2.5-flash",
+                contents=[uploaded_file, task_instruction],
+            )
+            return response.text
+
+        # Route 2: Hugging Face Serverless ASR fallback for audio if no Gemini key
+        if file_path.lower().endswith((".mp3", ".wav", ".m4a", ".flac")):
+            from huggingface_hub import InferenceClient
+            hf_client = InferenceClient(token=os.getenv("HF_TOKEN"))
+            result = hf_client.automatic_speech_recognition(file_path, model="openai/whisper-large-v3-turbo")
+            return f"Audio Transcription: {result.text}"
+
+        return "Multimodal inspection requires GEMINI_API_KEY secret for images or HF_TOKEN for audio."
+    except Exception as e:
+        return f"Error inspecting media file: {e}"
+
+
+# --- 3. Upgraded GAIAAgent with Planning & Two-Stage Answer Verification ---
+class GAIAAgent:
+    def __init__(self):
+        print("Initializing Upgraded GAIAAgent...")
+
+        if os.getenv("GEMINI_API_KEY"):
+            print("Using Gemini 2.5 Flash via LiteLLMModel")
+            self.model = LiteLLMModel(
+                model_id="gemini/gemini-2.5-flash",
+                api_key=os.getenv("GEMINI_API_KEY"),
+                temperature=0.0,
+            )
+        else:
+            print("Using Qwen/Qwen2.5-Coder-32B-Instruct via InferenceClientModel")
+            self.model = InferenceClientModel(
+                model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+                token=os.getenv("HF_TOKEN"),
+                temperature=0.0,
+            )
+
+        self.agent = CodeAgent(
+            tools=[
+                WebSearchTool(),
+                VisitWebpageTool(),
+                search_wikipedia,
+                get_youtube_transcript,
+                read_pdf_file,
+                read_text_or_code_file,
+                read_excel_or_csv,
+                inspect_image_or_audio,
+            ],
+            model=self.model,
+            additional_authorized_imports=[
+                "pandas", "numpy", "datetime", "math", "re", "json",
+                "csv", "openpyxl", "pypdf", "collections", "itertools",
+                "statistics", "urllib", "bs4"
+            ],
+            max_steps=12,
+            planning_interval=3,  # Re-evaluates plan every 3 steps so it never gets stuck
+        )
+
+    def clean_and_verify_answer(self, question: str, raw_answer: str) -> str:
+        """
+        Two-stage post-processor:
+        1. Uses an LLM formatter pass to ensure strict GAIA Exact-Match compliance.
+        2. Applies regex sanitization as a safety net.
+        """
+        raw_str = str(raw_answer).strip()
+
+        verification_prompt = f"""You are a strict GAIA benchmark answer extractor.
+Given the original QUESTION and the AGENT'S RAW OUTPUT, extract ONLY the exact final answer required by the question.
+
+RULES:
+1. Output ONLY the final answer string. Do NOT include any explanation, prefix, or "FINAL ANSWER:".
+2. If the question asks for a number/count, output ONLY digits (e.g., 14, not "14 albums" or "fourteen") unless units are explicitly requested.
+3. If the question asks for currency/money, follow the unit requested (e.g., if it asks "in USD" or "$", strip extra text).
+4. If the question asks for a comma-separated or alphabetical list, double-check the alphabetical/numerical sorting and use ", " (comma + single space) between items.
+5. Notice if the question is written backwards/reversed—if so, answer the decoded question directly.
+6. Do NOT wrap the output in quotes.
+
+QUESTION:
+{question}
+
+AGENT'S RAW OUTPUT:
+{raw_str}
+
+EXACT FINAL ANSWER:"""
+
+        try:
+            messages = [{"role": "user", "content": [{"type": "text", "text": verification_prompt}]}]
+            response = self.model(messages)
+            verified = response.content.strip()
+        except Exception:
+            verified = raw_str
+
+        # Final Regex Safety Net
+        ans = re.sub(r"^(final answer|exact final answer)\s*[:\-]?\s*", "", verified, flags=re.IGNORECASE).strip()
+        ans = ans.strip('"\'`')
+        if ans.endswith(".") and len(ans) < 50:
+            ans = ans[:-1].strip()
+        return ans
+
+    def __call__(self, question: str, file_path: str | None = None) -> str:
+        prompt = f"""You are an elite autonomous agent solving a GAIA benchmark question.
+
+STRATEGY GUIDELINES:
+1. Read the question carefully. If the text is reversed or encoded, write a 1-line Python snippet to reverse/decode it first!
+2. If a local file path is attached:
+   - For `.mp3`, `.wav`, `.png`, `.jpg`: call `inspect_image_or_audio(file_path, task_instruction=...)`.
+   - For `.xlsx`, `.csv`: call `read_excel_or_csv(file_path)` or write pandas code directly to filter/sum accurately.
+   - For `.pdf`: call `read_pdf_file(file_path)`.
+   - For `.py`, `.txt`: call `read_text_or_code_file(file_path)`.
+3. If the question mentions a YouTube link, ALWAYS call `get_youtube_transcript` first.
+4. If the question asks about Wikipedia, biographies, albums, or historical facts, use `search_wikipedia` in addition to `web_search`.
+5. Always use Python code to compute math, counts, sorting, or filtering rather than doing mental math.
+
+QUESTION:
+{question}
+"""
+        if file_path:
+            prompt += f"\nATTACHED LOCAL FILE PATH: {file_path}\n"
+
+        prompt += """
+When calling `final_answer(...)`, pass ONLY the exact answer required by the question and nothing else.
+"""
+        try:
+            raw_result = self.agent.run(prompt)
+            cleaned = self.clean_and_verify_answer(question, raw_result)
+            print(f"Raw Output: {raw_result}  -->  ✅ Verified Final Answer: {cleaned}")
+            return cleaned
+        except Exception as e:
+            print(f"❌ Agent execution error: {e}")
+            return "Error"
+
+
+# --- 4. Helper to Download Attached Files via GET /files/{task_id} ---
+def download_task_file(api_url: str, task_id: str, file_name: str) -> str | None:
+    if not file_name:
+        return None
+    os.makedirs("downloads", exist_ok=True)
+    local_path = os.path.join("downloads", f"{task_id}_{file_name}")
+    if os.path.exists(local_path) and os.path.getsize(local_path) > 0:
+        return local_path
+
+    file_url = f"{api_url}/files/{task_id}"
+    try:
+        print(f"Downloading attachment from {file_url} ...")
+        resp = requests.get(file_url, timeout=30)
+        if resp.status_code == 200:
+            with open(local_path, "wb") as f:
+                f.write(resp.content)
+            return local_path
+    except Exception as e:
+        print(f"Warning: Failed to download file for task {task_id}: {e}")
+    return None
+
+
+# --- 5. Cache Helpers (Avoid Re-Running Solved Questions) ---
+def load_cache() -> dict:
+    if os.path.exists(CACHE_FILE):
+        try:
+            with open(CACHE_FILE, "r", encoding="utf-8") as f:
+                return json.load(f)
+        except Exception:
+            return {}
+    return {}
+
+
+def save_cache(cache: dict):
+    with open(CACHE_FILE, "w", encoding="utf-8") as f:
+        json.dump(cache, f, indent=2, ensure_ascii=False)
+
+
+# --- 6. Main Evaluation & Submission Loop ---
+def run_and_submit_all(profile: gr.OAuthProfile | None, use_cache: bool = True):
+    space_id = os.getenv("SPACE_ID")
+
+    if profile:
+        username = f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        return "⚠️ Please Login to Hugging Face with the button above first!", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    try:
+        agent = GAIAAgent()
+    except Exception as e:
+        return f"Error initializing agent: {e}", None
+
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "https://huggingface.co/spaces"
+
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=30)
+        response.raise_for_status()
+        questions_data = response.json()
+    except Exception as e:
+        return f"Error fetching questions: {e}", None
+
+    cache = load_cache() if use_cache else {}
+    results_log = []
+    answers_payload = []
+
+    for idx, item in enumerate(questions_data, 1):
+        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:
+            continue
+
+        print(f"\n--- [{idx}/{len(questions_data)}] Task {task_id} ---")
+
+        # Use cached answer if available and valid
+        if use_cache and task_id in cache and cache[task_id] not in ("Error", "", None):
+            submitted_answer = cache[task_id]
+            print(f"⚡ Loaded from cache: {submitted_answer}")
+        else:
+            local_file = download_task_file(api_url, task_id, file_name)
+            submitted_answer = agent(question_text, file_path=local_file)
+            cache[task_id] = submitted_answer
+            save_cache(cache)
+            time.sleep(2)  # Gentle pause to prevent API/search rate-limiting
+
+        answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+        results_log.append({
+            "Task ID": task_id,
+            "Question": question_text,
+            "File": file_name or "None",
+            "Submitted Answer": submitted_answer,
+        })
+
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload,
+    }
+
+    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.')}"
+        )
+        return final_status, pd.DataFrame(results_log)
+    except Exception as e:
+        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+
+
+# --- 7. Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 🕵🏻‍♂️ Upgraded Multimodal GAIA Agent Runner")
+    gr.Markdown(
+        "1. Log in to your Hugging Face account using the button below.\n"
+        "2. Click **Run Evaluation & Submit All Answers** to run the multimodal `CodeAgent` with Wikipedia, YouTube, Audio/Vision, and Two-Stage Exact-Match Verification."
+    )
+    gr.LoginButton()
+    use_cache_checkbox = gr.Checkbox(
+        value=True,
+        label="Use local answer cache (skips questions already solved in previous runs)"
+    )
+    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=6, interactive=False)
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        inputs=[gr.State(), use_cache_checkbox],
+        outputs=[status_output, results_table],
+    )
+
+if __name__ == "__main__":
     demo.launch(debug=True, share=False)