langgraph_agent.py

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

from PIL import Image as PILImage  # Keep PIL for potential future use or if other parts depend on it, but describe_image is removed.
from huggingface_hub import InferenceClient  # Keep InferenceClient for other potential HF uses, but describe_image is removed.

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.document_loaders import WikipediaLoader
from langchain_core.messages import SystemMessage, HumanMessage, ToolMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.tools import tool
from langchain_google_community import GoogleSearchAPIWrapper

@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=5, lang="en", doc_content_chars_max=7000).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)}"}

search = GoogleSearchAPIWrapper()

@tool
def google_web_search(query: str) -> str:
    """Perform a web search (via Google Custom Search) and return results."""
    try:
        return search.run(query)
    except Exception as e:
        print(f"Error in google_web_search tool: {e}")
        return f"Error occurred while searching the web for '{query}'. Details: {str(e)}"


HF_API_TOKEN = os.getenv("HF_API_TOKEN")
MODEL = os.getenv("MODEL")
HF_INFERENCE_CLIENT = None
if HF_API_TOKEN:
    HF_INFERENCE_CLIENT = InferenceClient(token=HF_API_TOKEN)
else:
    print("WARNING: HF_API_TOKEN not set. If any other HF tools are used, they might 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, indicates it's a blob for LLM processing."""
    try:
        _, file_extension = os.path.splitext(file_path)
        file_extension = file_extension.lower()

        # Prioritize handling of video, audio, and image files for direct LLM processing
        if file_extension in (".mp4", ".avi", ".mov", ".mkv", ".webm"):
            return {"file_type": "video", "file_name": file_path, "file_content": f"Video file '{file_path}' detected. The LLM (Gemini 2.5 Pro) can process this video content directly as a blob."}
        elif file_extension == ".mp3":
            return {"file_type": "audio", "file_name": file_path, "file_content": f"Audio file '{file_path}' detected. The LLM (Gemini 2.5 Pro) can process this audio content directly as a blob."}
        elif file_extension in (".jpeg", ".jpg", ".png"):
            return {"file_type": "image", "file_name": file_path, "file_content": f"Image file '{file_path}' detected. The LLM (Gemini 2.5 Pro) can process this image content directly as a blob."}
        
        # Handle text and code files
        elif 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}
        
        # Handle Excel files
        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}
        
        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, .mp4, .avi, .mov, .mkv, .webm 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)}

API_KEY = os.getenv("GEMINI_API_KEY")
HF_API_TOKEN = os.getenv("HF_SPACE_TOKEN")  # Kept for potential future HF uses, but not for describe_image
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")

# Updated tools list without Youtube
tools = [
    multiply, add, subtract, divide, modulus,
    wiki_search,
    google_web_search,
    read_file_content,
    python_interpreter,
]

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"):
    if provider == "gemini":
        llm = ChatGoogleGenerativeAI(
            model=MODEL,
            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"]

        llm_response = llm_with_tools.invoke(messages_to_send, {"recursion_limit": 25})
        print(f"LLM Raw Response: {llm_response}")
        return {"messages": [llm_response]}

    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__":
    pass