Update app.py

app.py

@@ -1,25 +1,418 @@
+# 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.")
-
-    # CORRECCIÓN: Usa los .value, no strings
-    from google.genai.types import HarmCategory, HarmBlockThreshold
-
+    
+    # Using INTEGER VALUES for HarmCategory keys and HarmBlockThreshold enum .value for values.
     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,  # AQUÍ EL CAMBIO
+            safety_settings=llm_safety_settings_corrected_final, # USE THE DICTIONARY WITH INT VALUES FOR BOTH
             timeout=120,
             convert_system_message_to_human=True
         )
@@ -62,6 +455,7 @@ 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": []}
@@ -112,3 +506,169 @@ 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)