Update app.py

app.py

@@ -1,415 +1,25 @@
-# Copyright 2025 Jesus Vilela Jato.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# --- Imports ---
-import os
-import gradio as gr
-import requests
-import pandas as pd
-import json
-import logging
-from typing import Optional, List, Dict, Any, Tuple, Union, Type, TYPE_CHECKING, Annotated
-import hashlib
-from urllib.parse import urlparse
-import mimetypes
-import subprocess # For yt-dlp
-import io # For BytesIO with PIL
-
-# --- Global Variables for Startup Status ---
-missing_vars_startup_list_global = []
-agent_pre_init_status_msg_global = "Agent status will be determined at startup."
-
-# File Processing Libs
-try: from PyPDF2 import PdfReader; PYPDF2_AVAILABLE = True
-except ImportError: PYPDF2_AVAILABLE = False; print("WARNING: PyPDF2 not found, PDF tool will be disabled.")
-try: from PIL import Image; import pytesseract; PIL_TESSERACT_AVAILABLE = True
-except ImportError: PIL_TESSERACT_AVAILABLE = False; print("WARNING: Pillow or Pytesseract not found, OCR tool will be disabled.")
-try: import whisper; WHISPER_AVAILABLE = True
-except ImportError: WHISPER_AVAILABLE = False; print("WARNING: OpenAI Whisper not found, Audio Transcription tool will be disabled.")
-
-# --- google-genai SDK (Unified SDK) ---
-from google import genai as google_genai_sdk # Alias for clarity
-from google.genai.types import HarmCategory, HarmBlockThreshold # ***** CORRECTED IMPORT *****
-# For FileState enum later
-from google.ai import generativelanguage as glm
-# --- End google-genai SDK ---
-
-
-# LangChain
-from langchain_core.messages import HumanMessage, AIMessage, SystemMessage, ToolMessage
-from langchain.prompts import PromptTemplate
-from langchain.tools import BaseTool, tool as lc_tool_decorator
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.agents import AgentExecutor, create_react_agent
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_experimental.tools import PythonREPLTool
-
-# LangGraph Conditional Imports
-if TYPE_CHECKING:
-    from langgraph.graph import StateGraph as StateGraphAliasedForHinting
-    from langgraph.prebuilt import ToolNode as ToolExecutorAliasedForHinting
-    from typing_extensions import TypedDict
-    from langgraph.checkpoint.base import BaseCheckpointSaver
-
-LANGGRAPH_FLAVOR_AVAILABLE = False
-LG_StateGraph: Optional[Type[Any]] = None
-LG_ToolExecutor_Class: Optional[Type[Any]] = None
-LG_END: Optional[Any] = None
-LG_ToolInvocation: Optional[Type[Any]] = None
-add_messages: Optional[Any] = None
-MemorySaver_Class: Optional[Type[Any]] = None
-
-AGENT_INSTANCE: Optional[Union[AgentExecutor, Any]] = None
-TOOLS: List[BaseTool] = []
-LLM_INSTANCE: Optional[ChatGoogleGenerativeAI] = None
-LANGGRAPH_MEMORY_SAVER: Optional[Any] = None
-
-google_genai_client: Optional[google_genai_sdk.Client] = None # For direct SDK calls
-
-try:
-    from langgraph.graph import StateGraph, END
-    try:
-        from langgraph.prebuilt import ToolNode
-        LG_ToolExecutor_Class = ToolNode
-        print("Using langgraph.prebuilt.ToolNode for LangGraph tool execution.")
-    except ImportError:
-        try:
-            from langgraph.prebuilt import ToolExecutor
-            LG_ToolExecutor_Class = ToolExecutor
-            print("Using langgraph.prebuilt.ToolExecutor (fallback) for LangGraph tool execution.")
-        except ImportError as e_lg_exec_inner:
-             print(f"Failed to import ToolNode and ToolExecutor from langgraph.prebuilt: {e_lg_exec_inner}")
-             LG_ToolExecutor_Class = None
-
-    if LG_ToolExecutor_Class is not None:
-        from langgraph.prebuilt import ToolInvocation as LGToolInvocationActual
-        from langgraph.graph.message import add_messages as lg_add_messages
-        from langgraph.checkpoint.memory import MemorySaver as LGMemorySaver
-        LANGGRAPH_FLAVOR_AVAILABLE = True
-        LG_StateGraph, LG_END, LG_ToolInvocation, add_messages, MemorySaver_Class = \
-            StateGraph, END, LGToolInvocationActual, lg_add_messages, LGMemorySaver
-        print("Successfully imported LangGraph components.")
-    else:
-        LANGGRAPH_FLAVOR_AVAILABLE = False
-        LG_StateGraph, LG_END, LG_ToolInvocation, add_messages, MemorySaver_Class = (None,) * 5
-        print(f"WARNING: No suitable LangGraph tool executor (ToolNode/ToolExecutor) found. LangGraph agent will be disabled.")
-
-except ImportError as e: # Catch import error for StateGraph, END itself
-    LANGGRAPH_FLAVOR_AVAILABLE = False
-    LG_StateGraph, LG_ToolExecutor_Class, LG_END, LG_ToolInvocation, add_messages, MemorySaver_Class = (None,) * 6
-    print(f"WARNING: Core LangGraph components (StateGraph, END) not found or import error: {e}. LangGraph agent will be disabled.")
-
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-GEMINI_MODEL_NAME = "gemini-2.5-pro-preview-05-06"
-GEMINI_FLASH_MULTIMODAL_MODEL_NAME = "gemini-2.0-flash-exp"
-SCORING_API_BASE_URL = os.getenv("SCORING_API_URL", DEFAULT_API_URL)
-MAX_FILE_SIZE_BYTES = 50 * 1024 * 1024
-LOCAL_FILE_STORE_PATH = "./Data"
-os.makedirs(LOCAL_FILE_STORE_PATH, exist_ok=True)
-
-# --- Global State ---
-WHISPER_MODEL: Optional[Any] = None
-
-# --- Environment Variables & API Keys ---
-GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
-HUGGINGFACE_TOKEN = os.environ.get("HF_TOKEN")
-
-# --- Setup Logging ---
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(name)s - %(module)s:%(lineno)d - %(message)s')
-logger = logging.getLogger(__name__)
-
-# --- Initialize google-genai Client SDK ---
-if GOOGLE_API_KEY:
-    try:
-        google_genai_client = google_genai_sdk.Client(api_key=GOOGLE_API_KEY) # Using the aliased import
-        logger.info("google-genai SDK Client initialized successfully.")
-    except Exception as e:
-        logger.error(f"Failed to initialize google-genai SDK Client: {e}")
-        google_genai_client = None
-else:
-    logger.warning("GOOGLE_API_KEY not found. google-genai SDK Client not initialized.")
-
-# --- Helper Functions (Unchanged) ---
-def _strip_exact_match_answer(text: Any) -> str:
-    # ... (Your original _strip_exact_match_answer function)
-    if not isinstance(text, str): text = str(text)
-    text_lower_check = text.lower()
-    if text_lower_check.startswith("final answer:"):
-        text = text[len("final answer:"):].strip()
-    text = text.strip()
-    if text.startswith("```") and text.endswith("```"):
-        if "\n" in text:
-            text_content = text.split("\n", 1)[1] if len(text.split("\n", 1)) > 1 else ""
-            text = text_content.strip()[:-3].strip() if text_content.strip().endswith("```") else text[3:-3].strip()
-        else: text = text[3:-3].strip()
-    elif text.startswith("`") and text.endswith("`"): text = text[1:-1].strip()
-    if (text.startswith('"') and text.endswith('"')) or \
-       (text.startswith("'") and text.endswith("'")):
-        if len(text) > 1: text = text[1:-1]
-    return text.strip()
-
-def _is_full_url(url_string: str) -> bool:
-    # ... (Your original _is_full_url function)
-    try: result = urlparse(url_string); return all([result.scheme, result.netloc])
-    except ValueError: return False
-
-def _is_youtube_url(url: str) -> bool:
-    # ... (Your original _is_youtube_url function)
-    parsed_url = urlparse(url)
-    return parsed_url.netloc.lower().endswith(("youtube.com", "youtu.be"))
-
-def _download_file(file_identifier: str, task_id_for_file: Optional[str] = None) -> str:
-    # ... (Your original _download_file function - unchanged) ...
-    os.makedirs(LOCAL_FILE_STORE_PATH, exist_ok=True)
-    logger.debug(f"Download request: '{file_identifier}', task_id: {task_id_for_file}")
-    original_filename = os.path.basename(urlparse(file_identifier).path) if _is_full_url(file_identifier) else os.path.basename(file_identifier)
-    if not original_filename or original_filename == '/':
-        original_filename = hashlib.md5(file_identifier.encode()).hexdigest()[:12] + ".download"
-    prefix = f"{task_id_for_file}_" if task_id_for_file else ""
-    sanitized_original_filename = "".join(c if c.isalnum() or c in ['.', '_', '-'] else '_' for c in original_filename)
-    tentative_local_path = os.path.join(LOCAL_FILE_STORE_PATH, f"{prefix}{sanitized_original_filename}")
-
-    if _is_full_url(file_identifier) and _is_youtube_url(file_identifier):
-        logger.info(f"YouTube URL: {file_identifier}. Using yt-dlp.")
-        yt_file_hash = hashlib.md5(file_identifier.encode()).hexdigest()[:10]
-        yt_filename_base = f"youtube_{prefix}{yt_file_hash}"
-        target_mp3_path = os.path.join(LOCAL_FILE_STORE_PATH, yt_filename_base + ".mp3")
-        if os.path.exists(target_mp3_path) and os.path.getsize(target_mp3_path) > 0:
-            logger.info(f"Cached YouTube MP3: {target_mp3_path}"); return target_mp3_path
-        temp_output_template = os.path.join(LOCAL_FILE_STORE_PATH, yt_filename_base + "_temp.%(ext)s")
-        try:
-            command = ['yt-dlp', '--quiet', '--no-warnings', '-x', '--audio-format', 'mp3',
-                       '--audio-quality', '0', '--max-filesize', str(MAX_FILE_SIZE_BYTES),
-                       '-o', temp_output_template, file_identifier]
-            logger.info(f"yt-dlp command: {' '.join(command)}")
-            process = subprocess.run(command, capture_output=True, text=True, timeout=180, check=False)
-            downloaded_temp_file = next((os.path.join(LOCAL_FILE_STORE_PATH, f) for f in os.listdir(LOCAL_FILE_STORE_PATH)
-                                         if f.startswith(yt_filename_base + "_temp") and f.endswith(".mp3")), None)
-            if process.returncode == 0 and downloaded_temp_file and os.path.exists(downloaded_temp_file):
-                os.rename(downloaded_temp_file, target_mp3_path)
-                logger.info(f"yt-dlp success: {target_mp3_path}"); return target_mp3_path
-            else:
-                err_msg = process.stderr.strip() if process.stderr else "Unknown yt-dlp error"
-                logger.error(f"yt-dlp failed. RC:{process.returncode}. File:{downloaded_temp_file}. Err:{err_msg[:500]}")
-                if downloaded_temp_file and os.path.exists(downloaded_temp_file): os.remove(downloaded_temp_file)
-                return f"Error: yt-dlp failed. Msg:{err_msg[:200]}"
-        except Exception as e: logger.error(f"yt-dlp exception: {e}", exc_info=True); return f"Error: yt-dlp exception: {str(e)[:200]}"
-
-    file_url_to_try = file_identifier if _is_full_url(file_identifier) else None
-    if not file_url_to_try and task_id_for_file:
-        file_url_to_try = f"{SCORING_API_BASE_URL.rstrip('/')}/files/{task_id_for_file}"
-    elif not file_url_to_try:
-        if os.path.exists(file_identifier): logger.info(f"Using local file: {file_identifier}"); return file_identifier
-        return f"Error: Not URL, not local, no task_id for '{file_identifier}'."
-
-    if os.path.exists(tentative_local_path) and os.path.getsize(tentative_local_path) > 0:
-        logger.info(f"Cached file (pre-CD): {tentative_local_path}"); return tentative_local_path
-    effective_save_path = tentative_local_path
-    try:
-        auth_headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"} if HUGGINGFACE_TOKEN and \
-                       any(s in file_url_to_try for s in [SCORING_API_BASE_URL, ".hf.space", "huggingface.co"]) else {}
-        logger.info(f"Standard download: {file_url_to_try} (Headers: {list(auth_headers.keys())})")
-        with requests.get(file_url_to_try, stream=True, headers=auth_headers, timeout=60) as r:
-            r.raise_for_status()
-            cd_header = r.headers.get('content-disposition')
-            filename_from_cd = None
-            if cd_header:
-                try:
-                    decoded_cd_header = cd_header.encode('latin-1', 'replace').decode('utf-8', 'replace')
-                    _, params = requests.utils.parse_header_links(decoded_cd_header) # type: ignore
-                    for key, val in params.items():
-                        if key.lower() == 'filename*' and val.lower().startswith("utf-8''"):
-                            filename_from_cd = requests.utils.unquote(val[len("utf-8''"):]); break
-                        elif key.lower() == 'filename':
-                            filename_from_cd = requests.utils.unquote(val)
-                            if filename_from_cd.startswith('"') and filename_from_cd.endswith('"'): filename_from_cd = filename_from_cd[1:-1]
-                            break
-                except Exception as e_cd: logger.warning(f"CD parse error '{cd_header}': {e_cd}")
-            if filename_from_cd:
-                sanitized_cd_filename = "".join(c if c.isalnum() or c in ['.', '_', '-'] else '_' for c in filename_from_cd)
-                effective_save_path = os.path.join(LOCAL_FILE_STORE_PATH, f"{prefix}{sanitized_cd_filename}")
-                logger.info(f"Using CD filename: '{sanitized_cd_filename}'. Path: {effective_save_path}")
-
-            name_without_ext, current_ext = os.path.splitext(effective_save_path)
-            if not current_ext:
-                content_type_header = r.headers.get('content-type', '')
-                content_type_val = content_type_header.split(';')[0].strip() if content_type_header else ''
-                if content_type_val:
-                    guessed_ext = mimetypes.guess_extension(content_type_val)
-                    if guessed_ext: effective_save_path += guessed_ext; logger.info(f"Added guessed ext: {guessed_ext}")
-
-            if effective_save_path != tentative_local_path and os.path.exists(effective_save_path) and os.path.getsize(effective_save_path) > 0:
-                logger.info(f"Cached file (CD name): {effective_save_path}"); return effective_save_path
-            with open(effective_save_path, "wb") as f_download:
-                for chunk in r.iter_content(chunk_size=1024*1024): f_download.write(chunk)
-            logger.info(f"File downloaded to {effective_save_path}"); return effective_save_path
-    except requests.exceptions.HTTPError as e:
-        err_msg = f"HTTP {e.response.status_code} for {file_url_to_try}. Detail: {e.response.text[:100]}"
-        logger.error(err_msg, exc_info=False); return f"Error downloading: {err_msg}"
-    except Exception as e:
-        logger.error(f"Download error for {file_url_to_try}: {e}", exc_info=True); return f"Error: {str(e)[:100]}"
-
-# --- Tool Function Definitions ---
-READ_PDF_TOOL_DESC = "Reads text content from a PDF file. Input: JSON '{\"file_identifier\": \"FILENAME_OR_URL\", \"task_id\": \"TASK_ID_IF_GAIA_FILENAME_ONLY\"}'. Returns extracted text."
-@lc_tool_decorator(description=READ_PDF_TOOL_DESC)
-def read_pdf_tool(action_input_json_str: str) -> str:
-    # ... (Your original read_pdf_tool logic)
-    if not PYPDF2_AVAILABLE: return "Error: PyPDF2 not installed."
-    try: data = json.loads(action_input_json_str); file_id, task_id = data.get("file_identifier"), data.get("task_id")
-    except Exception as e: return f"Error parsing JSON for read_pdf_tool: {e}. Input: {action_input_json_str}"
-    if not file_id: return "Error: 'file_identifier' missing."
-    path = _download_file(file_id, task_id)
-    if path.startswith("Error:"): return path
-    try:
-        text_content = "";
-        with open(path, "rb") as f_pdf:
-            reader = PdfReader(f_pdf)
-            if reader.is_encrypted:
-                try: reader.decrypt('')
-                except: return f"Error: PDF '{path}' encrypted."
-            for page_num in range(len(reader.pages)):
-                page = reader.pages[page_num]
-                text_content += page.extract_text() + "\n\n"
-        return text_content[:40000]
-    except Exception as e: return f"Error reading PDF '{path}': {e}"
-
-OCR_IMAGE_TOOL_DESC = "Extracts text from an image using OCR. Input: JSON '{\"file_identifier\": \"FILENAME_OR_URL\", \"task_id\": \"TASK_ID_IF_GAIA_FILENAME_ONLY\"}'. Returns extracted text."
-@lc_tool_decorator(description=OCR_IMAGE_TOOL_DESC)
-def ocr_image_tool(action_input_json_str: str) -> str:
-    # ... (Your original ocr_image_tool logic)
-    if not PIL_TESSERACT_AVAILABLE: return "Error: Pillow/Pytesseract not installed."
-    try: data = json.loads(action_input_json_str); file_id, task_id = data.get("file_identifier"), data.get("task_id")
-    except Exception as e: return f"Error parsing JSON for ocr_image_tool: {e}. Input: {action_input_json_str}"
-    if not file_id: return "Error: 'file_identifier' missing."
-    path = _download_file(file_id, task_id)
-    if path.startswith("Error:"): return path
-    try: return pytesseract.image_to_string(Image.open(path))[:40000]
-    except Exception as e: return f"Error OCR'ing '{path}': {e}"
-
-TRANSCRIBE_AUDIO_TOOL_DESC = "Transcribes speech from an audio file (or YouTube URL) to text. Input: JSON '{\"file_identifier\": \"FILENAME_OR_URL_OR_YOUTUBE_URL\", \"task_id\": \"TASK_ID_IF_GAIA_FILENAME_ONLY\"}'. Returns transcript."
-@lc_tool_decorator(description=TRANSCRIBE_AUDIO_TOOL_DESC)
-def transcribe_audio_tool(action_input_json_str: str) -> str:
-    # ... (Your original transcribe_audio_tool logic)
-    global WHISPER_MODEL
-    if not WHISPER_AVAILABLE: return "Error: Whisper not installed."
-    try: data = json.loads(action_input_json_str); file_id, task_id = data.get("file_identifier"), data.get("task_id")
-    except Exception as e: return f"Error parsing JSON for transcribe_audio_tool: {e}. Input: {action_input_json_str}"
-    if not file_id: return "Error: 'file_identifier' missing."
-    if WHISPER_MODEL is None:
-        try: WHISPER_MODEL = whisper.load_model("base"); logger.info("Whisper 'base' model loaded.")
-        except Exception as e: logger.error(f"Whisper load failed: {e}"); return f"Error: Whisper load: {e}"
-    path = _download_file(file_id, task_id)
-    if path.startswith("Error:"): return path
-    try: result = WHISPER_MODEL.transcribe(path, fp16=False); return result["text"][:40000] # type: ignore
-    except Exception as e: logger.error(f"Whisper error on '{path}': {e}", exc_info=True); return f"Error transcribing '{path}': {e}"
-
-DIRECT_MULTIMODAL_GEMINI_TOOL_DESC = (
-    "Processes an image file (URL or local path) along with a text prompt using a Gemini multimodal model (gemini-2.0-flash-exp) "
-    "for tasks like image description, Q&A about the image, or text generation based on the image. "
-    "Input: JSON '{\"file_identifier\": \"IMAGE_FILENAME_OR_URL\", \"text_prompt\": \"Your question or instruction.\", \"task_id\": \"TASK_ID\" (optional)}'. "
-    "Returns the model's text response."
-)
-@lc_tool_decorator(description=DIRECT_MULTIMODAL_GEMINI_TOOL_DESC)
-def direct_multimodal_gemini_tool(action_input_json_str: str) -> str:
-    # ... (Implementation from previous response)
-    global google_genai_client
-    if not google_genai_client: return "Error: google-genai SDK client not initialized."
-    if not PIL_TESSERACT_AVAILABLE : return "Error: Pillow (PIL) library not available."
-    try:
-        data = json.loads(action_input_json_str)
-        file_identifier = data.get("file_identifier")
-        text_prompt = data.get("text_prompt", "Describe this image.")
-        task_id = data.get("task_id")
-        if not file_identifier: return "Error: 'file_identifier' for image missing."
-        logger.info(f"Direct Multimodal Tool: Image '{file_identifier}', Prompt '{text_prompt}'")
-        local_image_path = _download_file(file_identifier, task_id)
-        if local_image_path.startswith("Error:"): return f"Error downloading for Direct MM Tool: {local_image_path}"
-        try:
-            pil_image = Image.open(local_image_path)
-        except Exception as e_img_open: return f"Error opening image {local_image_path}: {str(e_img_open)}"
-        # Use the google_genai_client (which is google.genai.Client)
-        # For the client SDK, model names often don't need "models/" prefix if it's a tuned model or specific ID.
-        # If it's a base model, "models/" is usually required. Let's assume GEMINI_FLASH_MULTIMODAL_MODEL_NAME is a direct ID.
-        # However, to be safe with client.models.generate_content, using "models/" is more standard.
-        model_id_for_client = f"models/{GEMINI_FLASH_MULTIMODAL_MODEL_NAME}" if not GEMINI_FLASH_MULTIMODAL_MODEL_NAME.startswith("models/") else GEMINI_FLASH_MULTIMODAL_MODEL_NAME
-
-        response = google_genai_client.models.generate_content(
-            model=model_id_for_client,
-            contents=[pil_image, text_prompt]
-        )
-        logger.info(f"Direct Multimodal Tool: Response received from {model_id_for_client} received.")
-        return response.text[:40000]
-    except json.JSONDecodeError as e_json_mm: return f"Error parsing JSON for Direct MM Tool: {str(e_json_mm)}. Input: {action_input_json_str}"
-    except Exception as e_tool_mm:
-        logger.error(f"Error in direct_multimodal_gemini_tool: {e_tool_mm}", exc_info=True)
-        return f"Error executing Direct Multimodal Tool: {str(e_tool_mm)}"
-
-# --- Agent Prompts (Unchanged) ---
-LANGGRAPH_PROMPT_TEMPLATE_STR = """You are a highly intelligent agent for the GAIA benchmark.
-Your goal is to provide an EXACT MATCH final answer. No conversational text, explanations, or markdown unless explicitly part of the answer.
-TOOLS:
-You have access to the following tools. Use them if necessary.
-{tools}
-TOOL USAGE:
-- To use a tool, your response must include a `tool_calls` attribute in the AIMessage. Each tool call should be a dictionary with "name", "args" (a dictionary of arguments), and "id".
-- For file tools ('read_pdf_tool', 'ocr_image_tool', 'transcribe_audio_tool', 'direct_multimodal_gemini_tool'): `args` must contain 'file_identifier' (filename/URL) and 'task_id' (if GAIA file). For 'direct_multimodal_gemini_tool', also include 'text_prompt'.
-- 'web_search': `args` is like '{{"query": "search query"}}'.
-- 'python_repl': `args` is like '{{"command": "python code string"}}'. Use print() for output.
-RESPONSE FORMAT:
-Final AIMessage should contain ONLY the answer in 'content' and NO 'tool_calls'. If using tools, 'content' can be thought process, with 'tool_calls'.
-Begin!
-Current Task Details (including Task ID and any associated files):
-{input}"""
-
-REACT_PROMPT_TEMPLATE_STR = """You are a highly intelligent agent for the GAIA benchmark.
-Goal: EXACT MATCH answer. No extra text/markdown.
-Tools: {tools}
-Process: Question -> Thought -> Action (ONE of [{tool_names}]) -> Action Input -> Observation -> Thought ... -> Final Answer: [exact answer]
-Tool Inputs:
-- web_search: Your search query string.
-- python_repl: Python code string. Use print(). For Excel/CSV, use pandas: import pandas as pd; df = pd.read_excel('./Data/TASKID_filename.xlsx'); print(df.head())
-- read_pdf_tool, ocr_image_tool, transcribe_audio_tool: JSON string like '{{"file_identifier": "FILENAME_OR_URL", "task_id": "CURRENT_TASK_ID_IF_FILENAME"}}'.
-- direct_multimodal_gemini_tool: JSON string like '{{"file_identifier": "IMAGE_FILENAME_OR_URL", "text_prompt": "Your prompt for the image.", "task_id": "TASK_ID_IF_GAIA_FILENAME"}}'.
-If tool fails or info missing, Final Answer: N/A. Do NOT use unlisted tools.
-Begin!
-{input}
-Thought:{agent_scratchpad}"""
-
-
-# --- Agent Initialization and Response Logic ---
 def initialize_agent_and_tools(force_reinit=False):
     global AGENT_INSTANCE, TOOLS, LLM_INSTANCE, LANGGRAPH_FLAVOR_AVAILABLE, LG_StateGraph, LG_ToolExecutor_Class, LG_END, LG_ToolInvocation, add_messages, MemorySaver_Class, LANGGRAPH_MEMORY_SAVER, google_genai_client
     if AGENT_INSTANCE and not force_reinit: logger.info("Agent already initialized."); return
     logger.info("Initializing agent and tools...")
     if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY not set for LangChain LLM.")
-    
-    # Using INTEGER VALUES for HarmCategory keys and HarmBlockThreshold enum .value for values.
+
+    # CORRECCIÓN: Usa los .value, no strings
+    from google.genai.types import HarmCategory, HarmBlockThreshold
+
     llm_safety_settings_corrected_final = {
         HarmCategory.HARM_CATEGORY_HARASSMENT.value: HarmBlockThreshold.BLOCK_NONE.value,
         HarmCategory.HARM_CATEGORY_HATE_SPEECH.value: HarmBlockThreshold.BLOCK_NONE.value,
         HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT.value: HarmBlockThreshold.BLOCK_NONE.value,
         HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT.value: HarmBlockThreshold.BLOCK_NONE.value,
     }
-    
+
     try:
         LLM_INSTANCE = ChatGoogleGenerativeAI(
             model=GEMINI_MODEL_NAME,
             google_api_key=GOOGLE_API_KEY,
             temperature=0.0,
-            safety_settings=llm_safety_settings_corrected_final, # USE THE DICTIONARY WITH INT VALUES FOR BOTH
+            safety_settings=llm_safety_settings_corrected_final,  # AQUÍ EL CAMBIO
             timeout=120,
             convert_system_message_to_human=True
         )
@@ -452,7 +62,6 @@ def initialize_agent_and_tools(force_reinit=False):
             if not LG_ToolExecutor_Class: raise ValueError("LG_ToolExecutor_Class is None for LangGraph.")
             tool_executor_instance_lg = LG_ToolExecutor_Class(tools=TOOLS)
 
-
             def tool_node(state: AgentState):
                 last_msg = state['messages'][-1] if state.get('messages') and isinstance(state['messages'][-1], AIMessage) else None
                 if not last_msg or not last_msg.tool_calls: return {"messages": []}
@@ -503,169 +112,3 @@ def initialize_agent_and_tools(force_reinit=False):
 
     if not AGENT_INSTANCE: raise RuntimeError("CRITICAL: Agent initialization completely failed.")
     logger.info(f"Agent init finished. Active agent type: {type(AGENT_INSTANCE).__name__}")
-
-# --- get_agent_response, construct_prompt_for_agent, run_and_submit_all (Unchanged) ---
-def get_agent_response(prompt: str, task_id: Optional[str]=None, thread_id: Optional[str]=None) -> str:
-    # ... (Your original get_agent_response logic - unchanged) ...
-    global AGENT_INSTANCE, LLM_INSTANCE
-    thread_id_to_use = thread_id or (f"gaia_task_{task_id}" if task_id else hashlib.md5(prompt.encode()).hexdigest()[:8])
-    if not AGENT_INSTANCE or not LLM_INSTANCE:
-        logger.warning("Agent/LLM not initialized in get_agent_response. Attempting re-initialization.")
-        try: initialize_agent_and_tools(force_reinit=True)
-        except Exception as e_reinit_get: logger.error(f"Re-initialization failed: {e_reinit_get}"); return f"[ERROR] Agent/LLM re-init failed: {str(e_reinit_get)}"
-        if not AGENT_INSTANCE or not LLM_INSTANCE: return "[ERROR] Agent/LLM still None after re-init."
-    agent_name_get = type(AGENT_INSTANCE).__name__
-    logger.info(f"Agent ({agent_name_get}) processing. Task: {task_id or 'N/A'}. Thread: {thread_id_to_use}.")
-    is_langgraph_agent_get = LANGGRAPH_FLAVOR_AVAILABLE and AGENT_INSTANCE and hasattr(AGENT_INSTANCE, 'graph') and hasattr(AGENT_INSTANCE, 'config_schema')
-    try:
-        if is_langgraph_agent_get:
-            logger.debug(f"Using LangGraph agent (Memory: {LANGGRAPH_MEMORY_SAVER is not None}) for thread: {thread_id_to_use}")
-            initial_messages_lg_get = []
-            input_for_lg_get = {"input": prompt, "messages": initial_messages_lg_get}
-            final_state_lg_get = AGENT_INSTANCE.invoke(input_for_lg_get, {"configurable": {"thread_id": thread_id_to_use}}) # type: ignore
-            if not final_state_lg_get or 'messages' not in final_state_lg_get or not final_state_lg_get['messages']:
-                logger.error("LangGraph: No final state/messages."); return "[ERROR] LangGraph: No final state/messages."
-            for message_item_lg_get in reversed(final_state_lg_get['messages']):
-                if isinstance(message_item_lg_get, AIMessage) and not message_item_lg_get.tool_calls:
-                    return str(message_item_lg_get.content)
-            logger.warning("LangGraph: No suitable final AIMessage without tool_calls.")
-            return str(final_state_lg_get['messages'][-1].content) if final_state_lg_get['messages'] else "[ERROR] LangGraph: Empty messages."
-        elif isinstance(AGENT_INSTANCE, AgentExecutor):
-            logger.debug("Using ReAct agent.")
-            response_react_get = AGENT_INSTANCE.invoke({"input": prompt})
-            return str(response_react_get.get("output", "[ERROR] ReAct: No 'output' key."))
-        else:
-            logger.error(f"Unknown agent type: {agent_name_get}"); return f"[ERROR] Unknown agent type: {agent_name_get}"
-    except Exception as e_agent_run_get:
-        logger.error(f"Error during agent execution ({agent_name_get}): {e_agent_run_get}", exc_info=True)
-        return f"[ERROR] Agent execution failed: {str(e_agent_run_get)[:150]}"
-
-def construct_prompt_for_agent(q: Dict[str,Any]) -> str:
-    # ... (Your original construct_prompt_for_agent logic - unchanged) ...
-    tid,q_str=q.get("task_id","N/A"),q.get("question",""); files=q.get("files",[])
-    files_info = ("\nFiles:\n"+"\n".join([f"- {f} (task_id:{tid})"for f in files])) if files else ""
-    level = f"\nLevel:{q.get('level')}" if q.get('level') else ""
-    return f"Task ID:{tid}{level}{files_info}\n\nQuestion:{q_str}"
-
-def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
-    # ... (Your original run_and_submit_all logic - unchanged) ...
-    global AGENT_INSTANCE
-    space_id = os.getenv("SPACE_ID")
-    username_for_submission = None
-    if profile and hasattr(profile, 'username') and profile.username:
-        username_for_submission = profile.username
-        logger.info(f"Username from OAuth profile: {username_for_submission}")
-    else:
-        logger.warning("OAuth profile not available or username missing.")
-        return "Hugging Face login required. Please use the login button and try again.", None
-    if AGENT_INSTANCE is None:
-        try: logger.info("Agent not pre-initialized. Initializing for run..."); initialize_agent_and_tools()
-        except Exception as e: return f"Agent on-demand initialization failed: {e}", None
-        if AGENT_INSTANCE is None: return "Agent is still None after on-demand init.", None
-    agent_code_url_run=f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "local_dev_run"
-    questions_url_run,submit_url_run=f"{DEFAULT_API_URL}/questions",f"{DEFAULT_API_URL}/submit"
-    auth_headers_run={"Authorization":f"Bearer {HUGGINGFACE_TOKEN}"} if HUGGINGFACE_TOKEN else {}
-    try:
-        logger.info(f"Fetching questions from {questions_url_run}")
-        response_q_run=requests.get(questions_url_run,headers=auth_headers_run,timeout=30);response_q_run.raise_for_status();questions_data_run=response_q_run.json()
-        if not questions_data_run or not isinstance(questions_data_run,list):logger.error(f"Invalid questions data: {questions_data_run}");return "Fetched questions_data invalid.",None
-        logger.info(f"Fetched {len(questions_data_run)} questions.")
-    except Exception as e:logger.error(f"Fetch questions error: {e}",exc_info=True);return f"Fetch questions error:{e}",None
-    results_log_run,answers_payload_run=[],[]
-    logger.info(f"Running agent on {len(questions_data_run)} questions for user '{username_for_submission}'...")
-    for i,item_run in enumerate(questions_data_run):
-        task_id_run,question_text_run=item_run.get("task_id"),item_run.get("question")
-        if not task_id_run or question_text_run is None:logger.warning(f"Skipping item: {item_run}");continue
-        prompt_run=construct_prompt_for_agent(item_run);thread_id_run=f"gaia_batch_task_{task_id_run}"
-        logger.info(f"Processing Q {i+1}/{len(questions_data_run)} - Task: {task_id_run}")
-        try:
-            raw_answer_run=get_agent_response(prompt_run,task_id=task_id_run,thread_id=thread_id_run);submitted_answer_run=_strip_exact_match_answer(raw_answer_run)
-            answers_payload_run.append({"task_id":task_id_run,"submitted_answer":submitted_answer_run})
-            results_log_run.append({"Task ID":task_id_run,"Question":question_text_run,"Full Agent Prompt":prompt_run,"Raw Agent Output":raw_answer_run,"Submitted Answer":submitted_answer_run})
-        except Exception as e:
-            logger.error(f"Agent error task {task_id_run}:{e}",exc_info=True);error_answer_run=f"AGENT ERROR:{str(e)[:100]}"
-            answers_payload_run.append({"task_id":task_id_run,"submitted_answer":"N/A [AGENT_ERROR]"})
-            results_log_run.append({"Task ID":task_id_run,"Question":question_text_run,"Full Agent Prompt":prompt_run,"Raw Agent Output":error_answer_run,"Submitted Answer":"N/A [AGENT_ERROR]"})
-    if not answers_payload_run:return "Agent produced no answers.",pd.DataFrame(results_log_run)
-    submission_payload_run={"username":username_for_submission.strip(),"agent_code":agent_code_url_run,"answers":answers_payload_run}
-    logger.info(f"Submitting {len(answers_payload_run)} answers to {submit_url_run} for user '{username_for_submission}'...")
-    submission_headers_run={"Content-Type":"application/json",**auth_headers_run}
-    try:
-        response_s_run=requests.post(submit_url_run,json=submission_payload_run,headers=submission_headers_run,timeout=120);response_s_run.raise_for_status();submission_result_run=response_s_run.json()
-        result_message_run=(f"User:{submission_result_run.get('username',username_for_submission)}\nScore:{submission_result_run.get('score','N/A')}% ({submission_result_run.get('correct_count','?')}/{submission_result_run.get('total_attempted','?')})\nMsg:{submission_result_run.get('message','N/A')}")
-        logger.info(f"Submission OK! {result_message_run}");return f"Submission OK!\n{result_message_run}",pd.DataFrame(results_log_run,columns=["Task ID","Question","Full Agent Prompt","Raw Agent Output","Submitted Answer"])
-    except requests.exceptions.HTTPError as e:
-        error_http_run=f"HTTP {e.response.status_code}. Detail:{e.response.text[:200]}"; logger.error(f"Submit Fail:{error_http_run}",exc_info=True); return f"Submit Fail:{error_http_run}",pd.DataFrame(results_log_run)
-    except Exception as e:logger.error(f"Submit Fail unexpected:{e}",exc_info=True);return f"Submit Fail:{str(e)[:100]}",pd.DataFrame(results_log_run)
-
-# --- Build Gradio Interface ---
-with gr.Blocks(css=".gradio-container {max-width:1280px !important;margin:auto !important;}",theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# GAIA Agent Challenge Runner v7 (OAuth for Username)")
-    gr.Markdown(f"""**Instructions:**
-1. **Login with Hugging Face** using the button below. Your HF username will be used for submission.
-2. Click 'Run Evaluation & Submit' to process GAIA questions (typically 20).
-3. **Goal: 30%+ (6/20).** Agent uses Gemini Pro ({GEMINI_MODEL_NAME}) as planner. Tools include Web Search, Python, PDF, OCR, Audio/YouTube, and a new Direct Multimodal tool using Gemini Flash ({GEMINI_FLASH_MULTIMODAL_MODEL_NAME}).
-4. Ensure `GOOGLE_API_KEY` and `HUGGINGFACE_TOKEN` are Space secrets.
-5. Check Space logs for details. LangGraph is attempted (ReAct fallback).""")
-
-    agent_status_display = gr.Markdown("**Agent Status:** Initializing...")
-    missing_secrets_display = gr.Markdown("")
-
-    gr.LoginButton()
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=7, interactive=False)
-    results_table = gr.DataFrame(label="Q&A Log", headers=["Task ID","Question","Prompt","Raw","Submitted"], wrap=True) # Removed height
-
-    run_button.click(fn=run_and_submit_all, outputs=[status_output,results_table], api_name="run_evaluation")
-
-    def update_ui_on_load_fn_within_context():
-        global missing_vars_startup_list_global, agent_pre_init_status_msg_global
-        secrets_msg_md = ""
-        if missing_vars_startup_list_global:
-            secrets_msg_md = f"<font color='red'>**⚠️ Secrets Missing:** {', '.join(missing_vars_startup_list_global)}.</font>"
-        env_issues = []
-        try: subprocess.run(['yt-dlp','--version'],check=True,stdout=subprocess.DEVNULL,stderr=subprocess.DEVNULL)
-        except: env_issues.append("yt-dlp"); logger.warning("yt-dlp check failed (UI load).")
-        try: subprocess.run(['ffmpeg','-version'],check=True,stdout=subprocess.DEVNULL,stderr=subprocess.DEVNULL)
-        except: env_issues.append("ffmpeg"); logger.warning("ffmpeg check failed (UI load).")
-        if env_issues: secrets_msg_md += f"<br/><font color='orange'>**Tool Deps Missing:** {', '.join(env_issues)}.</font>"
-        current_status_md = agent_pre_init_status_msg_global
-        if not LANGGRAPH_FLAVOR_AVAILABLE and "LangGraph" not in current_status_md:
-             current_status_md += f" (LangGraph core components not fully loaded: LG_ToolExecutor_Class is {type(LG_ToolExecutor_Class).__name__ if LG_ToolExecutor_Class else 'None'}, ReAct fallback.)"
-        elif LANGGRAPH_FLAVOR_AVAILABLE and "LangGraph" not in current_status_md:
-             current_status_md += f" (LangGraph ready with {type(LG_ToolExecutor_Class).__name__ if LG_ToolExecutor_Class else 'UnknownExecutor'}.)"
-        return { agent_status_display: gr.Markdown(value=current_status_md),
-                 missing_secrets_display: gr.Markdown(value=secrets_msg_md) }
-
-    demo.load(update_ui_on_load_fn_within_context, [], [agent_status_display, missing_secrets_display])
-
-if __name__ == "__main__":
-    logger.info(f"Application starting up (v7 - Corrected GenAI Types Import)...")
-    if not PYPDF2_AVAILABLE: logger.warning("PyPDF2 (PDF tool) NOT AVAILABLE.")
-    if not PIL_TESSERACT_AVAILABLE: logger.warning("Pillow/Pytesseract (OCR tool) NOT AVAILABLE.")
-    if not WHISPER_AVAILABLE: logger.warning("Whisper (Audio tool) NOT AVAILABLE.")
-    if LANGGRAPH_FLAVOR_AVAILABLE: logger.info(f"Core LangGraph components (StateGraph, END, {type(LG_ToolExecutor_Class).__name__ if LG_ToolExecutor_Class else 'FailedExecutor'}) loaded.")
-    else: logger.warning("Core LangGraph FAILED import or essential component (ToolExecutor/Node) missing. ReAct fallback. Check requirements & Space build logs.")
-
-    missing_vars_startup_list_global.clear()
-    if not GOOGLE_API_KEY: missing_vars_startup_list_global.append("GOOGLE_API_KEY")
-    if not HUGGINGFACE_TOKEN: missing_vars_startup_list_global.append("HUGGINGFACE_TOKEN (for GAIA API)")
-
-    try:
-        logger.info("Pre-initializing agent...")
-        initialize_agent_and_tools()
-        if AGENT_INSTANCE:
-            agent_type_name = type(AGENT_INSTANCE).__name__
-            agent_pre_init_status_msg_global = f"Agent Pre-initialized: **{agent_type_name}**."
-            if LANGGRAPH_FLAVOR_AVAILABLE and ("StateGraph" in agent_type_name or "CompiledGraph" in agent_type_name) :
-                 lg_executor_display_name = type(LG_ToolExecutor_Class).__name__ if LG_ToolExecutor_Class else "UnknownExecutor"
-                 agent_pre_init_status_msg_global = f"Agent Pre-initialized: **LangGraph** (Executor: {lg_executor_display_name}, Memory: {LANGGRAPH_MEMORY_SAVER is not None})."
-        else: agent_pre_init_status_msg_global = "Agent pre-init FAILED (AGENT_INSTANCE is None)."
-        logger.info(agent_pre_init_status_msg_global.replace("**",""))
-    except Exception as e:
-        agent_pre_init_status_msg_global = f"Agent pre-init CRASHED: {str(e)}"
-        logger.critical(f"Agent pre-init CRASHED: {e}", exc_info=True)
-
-    logger.info(f"Space ID: {os.getenv('SPACE_ID', 'Not Set')}")
-    logger.info("Gradio Interface launching...")
-    demo.queue().launch(debug=os.getenv("GRADIO_DEBUG","false").lower()=="true", share=False, max_threads=20)