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Update app.py
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app.py
CHANGED
@@ -1,321 +1,147 @@
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from
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import gradio as gr
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from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage
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from langchain_openai import ChatOpenAI
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from langgraph.graph.message import add_messages
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from langgraph.graph import StateGraph, START
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from langgraph.prebuilt import tools_condition, ToolNode
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import requests
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import pandas as pd
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from langchain.tools import Tool
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from dotenv import load_dotenv
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from
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from
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from
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from
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load_dotenv()
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# (Keep Constants as is)
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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# --- Basic Agent Definition ---
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# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
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#search_tool = DuckDuckGoSearchRun()
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def
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response = requests.get(ASSOCIATED_FILE_ENDPOINT + task_id, timeout=15)
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response.raise_for_status()
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if response.status_code != 200:
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print(f"Error fetching file: {response.status_code}")
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return None
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#print(f"Fetched file: {response.content}")
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return response.content
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except requests.exceptions.RequestException as e:
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print(f"Error fetching file: {e}")
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return None
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except Exception as e:
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print(f"An unexpected error occurred fetching file: {e}")
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return None
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def retrieve_next_chess_move_in_algebraic_notation(task_file_path: str, is_black_turn: bool) -> str:
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"""
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Retrieve the next chess move in algebraic notation from an image path.
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"""
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if task_file_path is None:
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return "Error: Task file not found."
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# Retrieve the next chess move in algebraic notation
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next_chess_move = ChessAlgebraicNotationMoveRetriever().retrieve(task_file_path, is_black_turn)
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return next_chess_move
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# Initialize the tool
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retrieve_next_chess_move_in_algebraic_notation_tool = Tool(
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name="retrieve_next_chess_move_in_algebraic_notation",
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func=retrieve_next_chess_move_in_algebraic_notation,
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description="Retrieve the next chess move in algebraic notation from an image path."
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)
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def transcribe_audio(file_path: str) -> str:
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if file_path is None:
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return "Error: Audio path not found."
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# Transcribe the audio
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return Transcriber().transcribe(file_path)
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# Initialize the tool
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transcribe_audio_tool = Tool(
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name="transcribe_audio",
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func=transcribe_audio,
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description="Transcribe the audio from an audio path."
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)
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# Initialize the tool
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answer_python_code_tool = PythonCodeQuestionAnswerTool()
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# Initialize the tool
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answer_image_question_tool = ImageQuestionAnswerTool()
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# Initialize the tool
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answer_youtube_video_question_tool = YoutubeVideoQuestionAnswerTool()
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'''def answer_youtube_video_question(youtube_video_url: str, question: str) -> str:
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"""
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Answer the question based on the youtube video.
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"""
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if youtube_video_url is None:
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return "Error: Video not found."
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# Download the video
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video_path = YoutubeVideoDownloader().download_video(youtube_video_url)
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# Answer the question
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return VideoQuestionAnswer().answer(video_path, question)
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# Initialize the tool
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answer_youtube_video_question_tool = Tool(
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name="answer_youtube_video_question",
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func=answer_youtube_video_question,
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description="Answer the question based on the youtube video."
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)'''
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def read_excel_file(file_path: str) -> str:
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if file_path is None:
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return "Error: File not found."
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return ExcelFileReader().read_file(file_path)
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# Initialize the tool
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read_excel_file_tool = Tool(
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name="read_excel_file",
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func=read_excel_file,
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description="Read the excel file."
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)
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# Initialize the tool
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wikipedia_search_tool = Tool(
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name="wikipedia_search",
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func=WikipediaSearcher().search,
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description="Search Wikipedia for a given query."
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)
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# Initialize the tool
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arxiv_search_tool = Tool(
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name="arxiv_search",
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func=ArxivSearcher().search,
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description="Search Arxiv for a given query."
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)
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tavily_search_tool = Tool(
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name="tavily_search",
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func=TavilySearcher().search,
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description="Search the web for a given query."
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)
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def format_gaia_answer(answer: str) -> str:
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llm = ChatOpenAI(model="o3-mini", openai_api_key=os.getenv("OPENAI_API_KEY"))
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prompt = f"""
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You are formatting answers for the GAIA benchmark, which requires responses to be concise and unambiguous.
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Given the answer: {answer}
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Return the answer in the correct GAIA format:
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- If the answer is a single word or number, return it without any additional text or formatting.
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- If the answer is a list, return a comma-separated list without any additional text or formatting.
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- If the answer is a string, return it without any additional text or formatting.
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Do not include any prefixes, dots, enumerations, explanations, or quotation marks.
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Do not include any additional text or formatting.
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"""
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response = llm.invoke(prompt)
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# Delete double quotes
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return response.content.strip().replace('"', '')
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class AgentState(TypedDict):
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# The document provided
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messages: Annotated[list[AnyMessage], add_messages]
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file_path: Optional[str]
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class BasicAgent:
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def __init__(self):
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tools = [
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tavily_search_tool,
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arxiv_search_tool,
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wikipedia_search_tool,
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transcribe_audio_tool,
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answer_python_code_tool,
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answer_image_question_tool,
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answer_youtube_video_question_tool,
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read_excel_file_tool
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]
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'''llm = ChatGoogleGenerativeAI(
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model="gemini-2.0-flash",
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temperature=0.2,
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api_key=os.getenv("GEMINI_API_KEY")
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)'''
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llm = ChatOpenAI(model="o3-mini", openai_api_key=os.getenv("OPENAI_API_KEY"))
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self.llm_with_tools = llm.bind_tools(tools)
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builder = StateGraph(AgentState)
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# Define nodes: these do the work
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builder.add_node("assistant", self.assistant)
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builder.add_node("tools", ToolNode(tools))
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# Define edges: these determine how the control flow moves
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builder.add_edge(START, "assistant")
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builder.add_conditional_edges(
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"assistant",
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# If the latest message requires a tool, route to tools
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# Otherwise, provide a direct response
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tools_condition,
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)
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builder.add_edge("tools", "assistant")
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self.agent = builder.compile()
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print("BasicAgent initialized.")
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"""
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You have access to the file path of the attached file in case it's informed. Currently the file path is: {file_path}
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Be direct and specific. GAIA benchmark requires exact matching answers.
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For example, if asked "What is the capital of France?", respond simply with "Paris".
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Do not include any prefixes, dots, enumerations, explanations, or quotation marks.
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Do not include any additional text or formatting.
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If you are required a number, return a number, not the items.
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"""
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return {
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"messages": [self.llm_with_tools.invoke([sys_msg] + state["messages"], config={"configurable": {"file_path": state["file_path"]}})],
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"file_path": state["file_path"]
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}
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'''return {
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"messages": [self.llm_with_tools.invoke(
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state["messages"],
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config={"configurable": {"file_path": state["file_path"]}} # Aquí pasas el task_id
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)],
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"file_path": state["file_path"]
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}'''
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def __call__(self, question: str, task_id: str, file_name: str) -> str:
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print(f"######################### Agent received question (first 50 chars): {question[:50]}... with file_name: {file_name}")
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# Get the file path
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tmp_file_path = None
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if file_name is not None and file_name != "":
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file_content = retrieve_task_file(task_id)
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if file_content is not None:
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print(f"Saving file {file_name} to tmp folder")
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tmp_file_path = f"tmp/{file_name}"
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with open(tmp_file_path, "wb") as f:
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f.write(file_content)
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# Show the file path
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print(f"File path: {tmp_file_path}")
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messages = self.agent.invoke({"messages": [HumanMessage(question)], "file_path": tmp_file_path})
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# Show the messages
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for m in messages['messages']:
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m.pretty_print()
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answer = messages["messages"][-1].content
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answer = format_gaia_answer(answer)
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print(f"######################### Agent returning answer: {answer}\n")
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# Delete the file
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if tmp_file_path is not None:
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os.remove(tmp_file_path)
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return answer
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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from dotenv import load_dotenv
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import gradio as gr
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import requests
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from typing import List, Dict, Union, Optional
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import pandas as pd
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import wikipediaapi
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import requests
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#from bs4 import BeautifulSoup
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import random
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import re
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from typing import Optional
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from datetime import datetime
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import google.generativeai as genai
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load_dotenv()
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# (Keep Constants as is)
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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# --- Basic Agent Definition ---
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class BasicAgent:
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def __init__(self, model_name: str = "gemini-pro"):
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"""
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Multi-modal agent powered by Google Gemini with:
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- Web search
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- Wikipedia access
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- Document processing
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"""
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self.model = genai.GenerativeModel(model_name)
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self.wiki = wikipediaapi.Wikipedia('en')
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self.searx_url = "https://searx.space/search" # Public Searx instance
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print("BasicAgent initialized.")
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def __call__(self, question: str) -> str:
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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fixed_answer = self.process_request(question)
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print(f"Agent returning answer: {fixed_answer}")
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return fixed_answer
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def generate_response(self, prompt: str) -> str:
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"""Get response from Gemini"""
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try:
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response = self.model.generate_content(prompt)
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return response.text
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except Exception as e:
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return f"Error generating response: {str(e)}"
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def web_search(self, query: str) -> List[Dict]:
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"""Use SearxNG meta-search engine"""
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params = {
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"q": query,
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"format": "json",
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"engines": "google,bing,duckduckgo"
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}
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try:
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response = requests.get(self.searx_url, params=params)
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response.raise_for_status()
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return response.json().get("results", [])
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except requests.RequestException:
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return []
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def wikipedia_search(self, query: str) -> str:
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"""Get Wikipedia summary"""
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page = self.wiki.page(query)
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return page.summary if page.exists() else "No Wikipedia page found"
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74 |
+
|
75 |
+
def process_document(self, file_path: str) -> str:
|
76 |
+
"""Handle PDF, Word, CSV, Excel files"""
|
77 |
+
if not os.path.exists(file_path):
|
78 |
+
return "File not found"
|
79 |
+
|
80 |
+
ext = os.path.splitext(file_path)[1].lower()
|
81 |
+
|
82 |
+
try:
|
83 |
+
if ext == '.pdf':
|
84 |
+
return self._process_pdf(file_path)
|
85 |
+
elif ext in ('.doc', '.docx'):
|
86 |
+
return self._process_word(file_path)
|
87 |
+
elif ext == '.csv':
|
88 |
+
return pd.read_csv(file_path).to_string()
|
89 |
+
elif ext in ('.xls', '.xlsx'):
|
90 |
+
return pd.read_excel(file_path).to_string()
|
91 |
+
else:
|
92 |
+
return "Unsupported file format"
|
93 |
+
except Exception as e:
|
94 |
+
return f"Error processing document: {str(e)}"
|
95 |
+
|
96 |
+
def _process_pdf(self, file_path: str) -> str:
|
97 |
+
"""Process PDF using Gemini's vision capability"""
|
98 |
+
try:
|
99 |
+
# For Gemini 1.5 or later which supports file uploads
|
100 |
+
with open(file_path, "rb") as f:
|
101 |
+
file = genai.upload_file(f)
|
102 |
+
response = self.model.generate_content(
|
103 |
+
["Extract and summarize the key points from this document:", file]
|
104 |
+
)
|
105 |
+
return response.text
|
106 |
+
except:
|
107 |
+
# Fallback for older Gemini versions
|
108 |
+
try:
|
109 |
+
import PyPDF2
|
110 |
+
with open(file_path, 'rb') as f:
|
111 |
+
reader = PyPDF2.PdfReader(f)
|
112 |
+
return "\n".join([page.extract_text() for page in reader.pages])
|
113 |
+
except ImportError:
|
114 |
+
return "PDF processing requires PyPDF2 (pip install PyPDF2)"
|
115 |
+
|
116 |
+
def _process_word(self, file_path: str) -> str:
|
117 |
+
"""Process Word documents"""
|
118 |
+
try:
|
119 |
+
from docx import Document
|
120 |
+
doc = Document(file_path)
|
121 |
+
return "\n".join([para.text for para in doc.paragraphs])
|
122 |
+
except ImportError:
|
123 |
+
return "Word processing requires python-docx (pip install python-docx)"
|
124 |
+
|
125 |
+
def process_request(self, request: Union[str, Dict]) -> str:
|
126 |
"""
|
127 |
+
Handle different request types:
|
128 |
+
- Direct text queries
|
129 |
+
- File processing requests
|
130 |
+
- Complex multi-step requests
|
|
|
|
|
|
|
|
|
|
|
|
|
131 |
"""
|
132 |
+
if isinstance(request, dict):
|
133 |
+
if 'steps' in request:
|
134 |
+
results = []
|
135 |
+
for step in request['steps']:
|
136 |
+
if step['type'] == 'search':
|
137 |
+
results.append(self.web_search(step['query']))
|
138 |
+
elif step['type'] == 'process':
|
139 |
+
results.append(self.process_document(step['file']))
|
140 |
+
return self.generate_response(f"Process these results: {results}")
|
141 |
+
return "Unsupported request format"
|
142 |
+
|
143 |
+
return self.generate_response(request)
|
144 |
|
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|
145 |
|
146 |
def run_and_submit_all( profile: gr.OAuthProfile | None):
|
147 |
"""
|