agent.py

import os
from dotenv import load_dotenv
from typing import TypedDict, List, Dict, Any, Optional
from langgraph.graph import StateGraph, START, END, MessagesState
from langchain.agents import create_tool_calling_agent, AgentExecutor, initialize_agent, create_react_agent
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_core.tools import tool
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langgraph.prebuilt import ToolNode
from langgraph.prebuilt import tools_condition

# 1. Web Browsing
from langchain_community.tools import DuckDuckGoSearchResults
from langchain_community.document_loaders import ImageCaptionLoader
import requests, time
import pandas as pd
from pathlib import Path
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.document_loaders import YoutubeLoader
from langchain_community.document_loaders import UnstructuredExcelLoader
from langchain_community.document_loaders import AssemblyAIAudioTranscriptLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.utilities import GoogleSerperAPIWrapper

load_dotenv()
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

@tool
def duckduck_websearch(query: str) -> str:
    """Allows search through DuckDuckGo.
    Args:
        query: what you want to search
    """
    search = DuckDuckGoSearchResults()
    results = search.invoke(query)
    return "\n".join(results)

@tool
def serper_websearch(query: str) -> str:
    """Allows search through Serper.
    Args:
        query: what you want to search
    """
    search = GoogleSerperAPIWrapper(serper_api_key=os.getenv("SERPER_API_KEY"))
    results = search.run(query)
    return results

@tool
def visit_webpage(url: str) -> str:
    """Fetches raw HTML content of a web page.
    Args:
        url: the webpage url
    """
    try:
        response = requests.get(url, timeout=5)
        return response.text[:5000]
    except Exception as e:
        return f"[ERROR fetching {url}]: {str(e)}"

@tool
def wiki_search(query: str) -> str:
    """Wiki search tools.
    Args:
        query: what you want to wiki
    """
    api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)
    wikipediatool = WikipediaQueryRun(api_wrapper=api_wrapper)
    return wikipediatool.run({"query": query})

@tool
def text_splitter(text: str) -> List[str]:
    """Splits text into chunks using LangChain's CharacterTextSplitter.
    Args:
        text: A string of text to split.
    """
    splitter = CharacterTextSplitter(chunk_size=450, chunk_overlap=10)
    return splitter.split_text(text)

@tool
def youtube_transcript(video_url: str) -> str:
    """Fetched youtube transcript
    Args:
        video_url: YouTube video url
    """
    try:
        loader = YoutubeLoader.from_youtube_url(video_url)
        # video_id = video_url.split("v=")[-1].split("&")[0]
        # transcript = YouTubeTranscriptApi.get_transcript(video_id)
        return loader.load()
    except Exception as e:
        return f"Error fetching transcript: {str(e)}"

# 4. File Reading
@tool
def read_file(task_id: str) -> str:
    """First download the file, then read its content
    Args:
        dir: the task_id
    """
    file_url = f'{DEFAULT_API_URL}/files/{task_id}'
    r = requests.get(file_url, timeout=15, allow_redirects=True)
    with open('temp', "wb") as fp:
        fp.write(r.content)
    with open('temp') as f:
        return f.read()

@tool
def excel_read(task_id: str) -> str:
    """First download the excel file, then read its content
    Args:
        dir: the task_id
    """
    try:
        file_url = f'{DEFAULT_API_URL}/files/{task_id}'
        r = requests.get(file_url, timeout=15, allow_redirects=True)
        with open('temp.xlsx', "wb") as fp:
            fp.write(r.content)
        # Read the Excel file
        df = pd.read_excel('temp.xlsx')
        # Run various analyses based on the query
        result = (
            f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
        )
        result += f"Columns: {', '.join(df.columns)}\n\n"
        # Add summary statistics
        result += "Summary statistics:\n"
        result += str(df.describe())
        return result
    except Exception as e:
        return f"Error analyzing Excel file: {str(e)}"
   
@tool
def csv_read(task_id: str) -> str:
    """First download the csv file, then read its content
    Args:
        dir: the task_id
    """
    try:
        file_url = f'{DEFAULT_API_URL}/files/{task_id}'
        r = requests.get(file_url, timeout=15, allow_redirects=True)
        with open('temp.csv', "wb") as fp:
            fp.write(r.content)
        # Read the CSV file
        df = pd.read_csv(temp.csv)
        # Run various analyses based on the query
        result = (
            f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
        )
        result += f"Columns: {', '.join(df.columns)}\n\n"
        # Add summary statistics
        result += "Summary statistics:\n"
        result += str(df.describe())
        return result
    except Exception as e:
        return f"Error analyzing CSV file: {str(e)}"
        
@tool
def mp3_listen(task_id: str) -> str:
    """First download the mp3 file, then listen to it
    Args:
        dir: the task_id
    """
    file_url = f'{DEFAULT_API_URL}/files/{task_id}'
    r = requests.get(file_url, timeout=15, allow_redirects=True)
    with open('temp.mp3', "wb") as fp:
        fp.write(r.content)
    loader = AssemblyAIAudioTranscriptLoader(file_path="temp.mp3", api_key=os.getenv("AssemblyAI_API_KEY"))
    docs = loader.load()
    contents = [doc.page_content for doc in docs]
    return "\n".join(contents)
    
# 5. Image Open
@tool
def image_caption(dir: str) -> str:
    """Understand the content of the provided image
    Args:
        dir: the image url link
    """
    loader = ImageCaptionLoader(images=[dir])
    metadata = loader.load()
    return metadata[0].page_content

# 2. Coding
from langchain_experimental.tools import PythonREPLTool
@tool
def run_python(code: str):
    """ Run the given python code
    Args:
        code: the python code
    """
    return PythonREPLTool().run(code)

@tool
def multiply(a: float, b: float) -> float:
    """Multiply two numbers.
    Args:
        a: first float
        b: second float
    """
    return a * b

@tool
def add(a: float, b: float) -> float:
    """Add two numbers.
    Args:
        a: first float
        b: second float
    """
    return a + b

@tool
def subtract(a: float, b: float) -> float:
    """Subtract two numbers.
    Args:
        a: first float
        b: second float
    """
    return a - b

@tool
def divide(a: float, b: float) -> float:
    """Divide two numbers.
    Args:
        a: first float
        b: second float
    """
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

# 3. Multi-Modality
# - multiply: multiply two numbers, A and B
# - add: add two numbers, A and B
# - subtract: Subtract A by B with passing A as the first argument
# - divide: Divide A by B with passing A as the first argument

# You have access to the following tools:
# - serper_websearch: web search the content of the query by passing the query as input with Serper Search Engine
# - duckduck_websearch: web search the content of the query by passing the query as input with DuckDuckGo Search Engine
# - visit_webpage: visit the given webpage url by passing the url as input
# - wiki_search: wiki search the content of the query by passing the query as input if the question asks for wiki search it
# - text_splitter: split text into chunks
# - youtube_transcript: fetch the transcript of the Youtube video by passing the video url as input if the question asks for watching a Youtube video
# - read_file: read the content of the attached file by passing the TASK-ID as input
# - excel_read: read the content of the attached excel file by passing the TASK-ID as input
# - csv_read: read the content of the attached csv file by passing the TASK-ID as input
# - mp3_listen: listen to the content of the attached mp3 file by passing the TASK-ID as input
# - image_caption: understand the visual content of the attached image by passing the TASK-ID as input
# - run_python: run the python code

# ("human", f"Question: {question}\nReport to validate: {final_answer}")
class BasicAgent:
    def __init__(self):
        self.model = ChatGoogleGenerativeAI(
            model="gemini-2.0-flash-lite",
            temperature=0,
            max_tokens=128,
            timeout=None,
            max_retries=2,
            google_api_key=os.getenv("GEMINI_API_KEY"),
            # other params...
        )
        # self.model = ChatGroq(
        #     model="qwen-qwq-32b",
        #     temperature=0,
        #     max_tokens=128,
        #     timeout=None,
        #     max_retries=2,
        #     groq_api_key=os.getenv("GROQ_API_KEY")
        #     # other params...
        # )
        # System Prompt for few shot prompting
        self.sys_prompt = """"
                You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: 
                FINAL ANSWER: [YOUR FINAL ANSWER].
                YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separared list of numbers and/or strings.
                If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
                If you are asked for a string, don't use articles, neither abbreviations (eg. for cities), and write the digits in plain text unless specified otherwise.
                If you are asked for a comma separated list, apply the above rules depending of whether the element to put in the list is a number or a string.

                You have access to the following tools:
                {tools}
                Here are the tools you can use: {tool_names}
                If Task ID is included in the question, remember to call the relevant read tools [ie. read_file, excel_read, csv_read, mp3_listen, image_caption]
                Note: python_tool is called when the question mentions the term "Python" or any math calculation.

                Follow this format in your response:
                THOUGHT: [Describe your reasoning here]
                ACTION: [Specify the action/tool to use and any relevant input]
                OBSERVATIOn: [Result of the action/tool, provided by the system]
                FINAL ANSWER: [Provide your final response to the user]

                User Input: {input}
                {agent_scratchpad}
        """
        self.tools = [duckduck_websearch, serper_websearch, visit_webpage, wiki_search, text_splitter, youtube_transcript, read_file, excel_read, csv_read, mp3_listen, image_caption, run_python]
        # self.model_with_tools = self.model.bind_tools(self.tools)
        # self.sys_msg = SystemMessage(content=self.sys_prompt)
        
        # self.prompt = ChatPromptTemplate.from_messages([
        #     ("system", self.sys_prompt),
        #     ("human", "{input}")
        # ])
        self.prompt = PromptTemplate(
            input_variables=["input", "tools", "tool_names", "agent_scratchpad"],
            template=self.sys_prompt
        )
        # self.agent = initialize_agent(
        #     tools=self.tools,
        #     llm=self.model,
        #     agent="zero-shot-react-description",  # ReAct agent type
        #     verbose=True,
        #     system_prompt=self.prompt,
        #     handle_parsing_errors="Check your output and make sure it conforms, use the Action/Action Input syntax"
        # )
        self.agent = create_react_agent(
            llm=self.model,
            tools=self.tools,
            prompt=self.prompt
        )
        self.agent_exe = AgentExecutor(agent=self.agent, tools=self.tools, verbose=True,
                                      handle_parsing_errors="Check your output and make sure it conforms, use the Action/Action Input syntax")
        # self.graph = self.__graph_compile__()
        print("BasicAgent initialized.")
    
    def __call__(self, task: dict) -> str:
        task_id, question, file_name = task["task_id"], task["question"], task["file_name"]
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        if file_name == "" or file_name is None:
            question = question
        else:
            question = f"{question} with TASK-ID: {task_id}"
            # fixed_answer = self.agent.run(f'{question} with TASK-ID: {task_id}')
        # fixed_answer = "This is a default answer."
        # fixed_answer = self.agent.run(question)
        fixed_answer = self.agent_exe.invoke({"input": question})
        # human_message = [HumanMessage(content=question)]
        # messages = self.graph.invoke({"messages": human_message})
        # fixed_answer = messages['messages'][-1].content
        print(f"Agent returning fixed answer: {fixed_answer}")
        time.sleep(60)
        return fixed_answer

    def __graph_compile__(self):
        def assistant(state: MessagesState):
            """Assistant Node"""
            return {"message": [self.model_with_tools.invoke(state["messages"])]}
        
        builder = StateGraph(MessagesState)
        builder.add_node("assistant", assistant)
        builder.add_node("tools", ToolNode(self.tools))
        builder.add_edge(START, "assistant")
        builder.add_conditional_edges(
            "assistant",
            # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools
            # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END
            tools_condition,
        )
        builder.add_edge("tools", "assistant")
        # Compile graph
        return builder.compile()