langgraph_agent.py

import os
import io
import contextlib
import pandas as pd
from typing import Dict, List, Union

# New imports for image and audio processing
from PIL import Image as PILImage # Used for type checking/potential future local processing
from huggingface_hub import InferenceClient

from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition, ToolNode
from langchain_openai import ChatOpenAI
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.tools import tool

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers."""
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two integers."""
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract the second integer from the first."""
    return a - b

@tool
def divide(a: int, b: int) -> float:
    """Divide first integer by second; error if divisor is zero."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Return the remainder of dividing first integer by second."""
    return a % b

@tool
def wiki_search(query: str) -> dict:
    """Search Wikipedia for a query and return up to 2 documents."""
    try:
        docs = WikipediaLoader(query=query, load_max_docs=2, lang="en").load()
        if not docs:
            return {"wiki_results": f"No documents found on Wikipedia for '{query}'."}
        formatted = "\n\n---\n\n".join(
            f'<Document source="{d.metadata.get("source", "N/A")}"/>\n{d.page_content}'
            for d in docs
        )
        return {"wiki_results": formatted}
    except Exception as e:
        print(f"Error in wiki_search tool: {e}")
        return {"wiki_results": f"Error occurred while searching Wikipedia for '{query}'. Details: {str(e)}"}

@tool
def web_search(query: str) -> dict:
    """Perform a web search (via Tavily) and return up to 3 results."""
    try:
        docs = TavilySearchResults(max_results=3).invoke(query=query)
        formatted = "\n\n---\n\n".join(
            f'<Document source="{d.metadata["source"]}"/>\n{d.page_content}'
            for d in docs
        )
        return {"web_results": formatted}
    except Exception as e:
        print(f"Error in web_search tool: {e}")
        return {"web_results": f"Error occurred while searching the web for '{query}'. Details: {str(e)}"}

@tool
def arvix_search(query: str) -> dict:
    """Search arXiv for a query and return up to 3 paper excerpts."""
    docs = ArxivLoader(query=query, load_max_docs=3).load()
    formatted = "\n\n---\n\n".join(
        f'<Document source="{d.metadata["source"]}"/>\n{d.page_content[:1000]}'
        for d in docs
    )
    return {"arvix_results": formatted}

# Initialize Hugging Face Inference Client
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
HF_INFERENCE_CLIENT = None
if HF_API_TOKEN:
    HF_INFERENCE_CLIENT = InferenceClient(token=HF_API_TOKEN)
else:
    print("WARNING: HF_API_TOKEN not set. Image and Audio tools will not function.")

@tool
def read_file_content(file_path: str) -> Dict[str, str]:
    """
    Reads the content of a file and returns its primary information.
    For text/code/excel, returns content. For media, returns a prompt to use specific tools.
    """
    try:
        _, file_extension = os.path.splitext(file_path)
        file_extension = file_extension.lower()

        if file_extension in (".txt", ".py"):
            with open(file_path, "r", encoding="utf-8") as f:
                content = f.read()
            return {"file_type": "text/code", "file_name": file_path, "file_content": content}
        elif file_extension == ".xlsx":
             df = pd.read_excel(file_path)
             content = df.to_string()
             return {"file_type": "excel", "file_name": file_path, "file_content": content}
        elif file_extension in (".jpeg", ".jpg", ".png"):
            # Indicate that it's an image and needs to be described by a specific tool
            return {"file_type": "image", "file_name": file_path, "file_content": f"Image file '{file_path}' detected. Use 'describe_image' tool to get a textual description."}
        elif file_extension == ".mp3":
            # Indicate that it's an audio file and needs to be transcribed by a specific tool
            return {"file_type": "audio", "file_name": file_path, "file_content": f"Audio file '{file_path}' detected. Use 'transcribe_audio' tool to get the text transcription."}
        else:
            return {"file_type": "unsupported", "file_name": file_path, "file_content": f"Unsupported file type: {file_extension}. Only .txt, .py, .xlsx, .jpeg, .jpg, .png, .mp3 files are recognized."}
    except FileNotFoundError:
        return {"file_error": f"File not found: {file_path}. Please ensure the file exists in the environment."}
    except Exception as e:
        return {"file_error": f"Error reading file {file_path}: {e}"}

@tool
def python_interpreter(code: str) -> Dict[str, str]:
    """
    Executes Python code and returns its standard output.
    If there's an error during execution, it returns the error message.
    """
    old_stdout = io.StringIO()
    with contextlib.redirect_stdout(old_stdout):
        try:
            exec_globals = {}
            exec_locals = {}
            exec(code, exec_globals, exec_locals)
            output = old_stdout.getvalue()
            return {"execution_result": output.strip()}
        except Exception as e:
            return {"execution_error": str(e)}

@tool
def describe_image(image_path: str) -> Dict[str, str]:
    """
    Generates a textual description for an image file (JPEG, JPG, PNG) using an image-to-text model
    from the Hugging Face Inference API. Requires HF_API_TOKEN environment variable to be set.
    """
    if not HF_INFERENCE_CLIENT:
        return {"error": "Hugging Face API token not configured for image description. Cannot use this tool."}
    try:
        with open(image_path, "rb") as f:
            image_bytes = f.read()
        description = HF_INFERENCE_CLIENT.image_to_text(image_bytes)
        return {"image_description": description, "image_path": image_path}
    except FileNotFoundError:
        return {"error": f"Image file not found: {image_path}. Please ensure the file exists."}
    except Exception as e:
        return {"error": f"Error describing image {image_path}: {str(e)}"}

@tool
def transcribe_audio(audio_path: str) -> Dict[str, str]:
    """
    Transcribes an audio file (e.g., MP3) to text using an automatic speech recognition model
    from the Hugging Face Inference API. Requires HF_API_TOKEN environment variable to be set.
    """
    if not HF_INFERENCE_CLIENT:
        return {"error": "Hugging Face API token not configured for audio transcription. Cannot use this tool."}
    try:
        with open(audio_path, "rb") as f:
            audio_bytes = f.read()
        transcription = HF_INFERENCE_CLIENT.automatic_speech_recognition(audio_bytes)
        return {"audio_transcription": transcription, "audio_path": audio_path}
    except FileNotFoundError:
        return {"error": f"Audio file not found: {audio_path}. Please ensure the file exists."}
    except Exception as e:
        return {"error": f"Error transcribing audio {audio_path}: {str(e)}"}


API_KEY = os.getenv("GEMINI_API_KEY")
HF_SPACE_TOKEN = os.getenv("HF_SPACE_TOKEN")
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")


tools = [
    multiply, add, subtract, divide, modulus,
    wiki_search, web_search, arvix_search,
    read_file_content,
    python_interpreter,
    describe_image,
    transcribe_audio, # Re-added tool
]


with open("prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()
sys_msg = SystemMessage(content=system_prompt)


def build_graph(provider: str = "gemini"):
    """Build the LangGraph agent with chosen LLM (default: Gemini)."""
    if provider == "gemini":
        llm = ChatGoogleGenerativeAI(
        model= "gemini-1.5-flash-preview-05-20",
        temperature=1.0,
        max_retries=2,
        api_key=GEMINI_API_KEY,
        max_tokens=5000
)

    elif provider == "huggingface":
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
            ),
            temperature=0,
        )
    else:
        raise ValueError("Invalid provider. Choose 'gemini' or 'huggingface'.")

    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        messages_to_send = [sys_msg] + state["messages"]
        return {"messages": [llm_with_tools.invoke(messages_to_send)]}

    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    builder.add_edge(START, "assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")

    return builder.compile()

if __name__ == "__main__":
    # This block is intentionally left empty as per user request to remove examples.
    # Your agent will interact with the graph by invoking it with messages.
    pass