app.py

import os
import streamlit as st
from pathlib import Path
from tempfile import TemporaryDirectory
from langchain_core.messages import BaseMessage, HumanMessage
from typing import Annotated, List, Optional, Dict
from typing_extensions import TypedDict
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph, START
import functools
import operator
import logging
import time
from tenacity import retry, stop_after_attempt, wait_exponential, RetryError
from pydantic import ValidationError

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize temporary directory
if 'working_directory' not in st.session_state:
    _TEMP_DIRECTORY = TemporaryDirectory()
    st.session_state.working_directory = Path(_TEMP_DIRECTORY.name)

WORKING_DIRECTORY = st.session_state.working_directory

# Streamlit UI
st.set_page_config(page_title="MARS: Multi-Agent Report Synthesizer", layout="wide")

# Custom CSS for styling
st.markdown("""

    <style>

        body {

            background-color: #f5f5f5;

            color: #333333;

            font-family: 'Comic Sans MS', 'Comic Sans', cursive;

        }

        .report-container {

            border-radius: 10px;

            background-color: #ffcccb;

            padding: 20px;

        }

        .sidebar .sidebar-content {

            background-color: #333333;

            color: #ffffff;

        }

        .stButton button {

            background-color: #ff6347;

            color: #ffffff;

            border-radius: 5px;

            font-size: 18px;

            padding: 10px 20px;

            font-weight: bold;

        }

        .stTextInput input {

            border-radius: 5px;

            border: 2px solid #ff6347;

            font-size: 16px;

            padding: 10px;

            width: 100%;

        }

        .stTextInput label {

            font-size: 18px;

            font-weight: bold;

            color: #333333;

        }

        .stSelectbox label, .stDownloadButton label {

            font-size: 18px;

            font-weight: bold;

            color: #333333;

        }

        .stSelectbox div, .stDownloadButton div {

            background-color: #ffcccb;

            color: #333333;

            border-radius: 5px;

            padding: 10px;

            font-size: 16px;

        }

    </style>

""", unsafe_allow_html=True)

st.title("🚀 MARS: Multi-agent Report Synthesizer 🤖")
st.sidebar.title("📋 Instructions")
st.sidebar.write("""

1. Enter your query in the input box.

2. Marvin AI will assign tasks to different teams.

3. You can see the progress and download the final report.

4. Use the buttons to list and download output files.

""")

# Input fields for API keys
openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
tavily_api_key = st.sidebar.text_input("Tavily API Key", type="password")

# Store the API keys in the session state
if openai_api_key:
    os.environ["OPENAI_API_KEY"] = openai_api_key
if tavily_api_key:
    os.environ["TAVILY_API_KEY"] = tavily_api_key

# Check if the API keys are set
if not os.getenv("OPENAI_API_KEY"):
    st.error("OpenAI API Key is required.")
if not os.getenv("TAVILY_API_KEY"):
    st.error("Tavily API Key is required.")

# Define tools
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def tavily_search_with_retry(*args, **kwargs):
    try:
        result = TavilySearchResults(*args, **kwargs)
        return result
    except ValidationError as ve:
        logger.error(f"Validation error: {ve}")
        raise ve
    except Exception as e:
        logger.error(f"Error in Tavily search: {e}")
        raise e

tavily_tool = tavily_search_with_retry(max_results=5)

@tool
def scrape_webpages(urls: List[str]) -> str:
    """Use requests and bs4 to scrape the provided web pages for detailed information."""
    try:
        loader = WebBaseLoader(urls)
        docs = loader.load()
        return "\n\n".join(
            [
                f'\n{doc.page_content}\n'
                for doc in docs
            ]
        )
    except Exception as e:
        logger.error(f"Error in scrape_webpages: {str(e)}")
        return f"Error occurred while scraping webpages: {str(e)}"

@tool
def create_outline(

    points: Annotated[List[str], "List of main points or sections."],

    file_name: Annotated[str, "File path to save the outline."],

) -> Annotated[str, "Path of the saved outline file."]:
    """Create and save an outline."""
    try:
        with (WORKING_DIRECTORY / file_name).open("w") as file:
            for i, point in enumerate(points):
                file.write(f"{i + 1}. {point}\n")
        return f"Outline saved to {file_name}"
    except Exception as e:
        logger.error(f"Error in create_outline: {str(e)}")
        return f"Error occurred while creating outline: {str(e)}"

@tool
def read_document(

    file_name: Annotated[str, "File path to save the document."],

    start: Annotated[Optional[int], "The start line. Default is 0"] = None,

    end: Annotated[Optional[int], "The end line. Default is None"] = None,

) -> str:
    """Read the specified document."""
    try:
        with (WORKING_DIRECTORY / file_name).open("r") as file:
            lines = file.readlines()
        if start is not None:
            start = 0
        return "\n".join(lines[start:end])
    except Exception as e:
        logger.error(f"Error in read_document: {str(e)}")
        return f"Error occurred while reading document: {str(e)}"

@tool
def write_document(

    content: Annotated[str, "Text content to be written into the document."],

    file_name: Annotated[str, "File path to save the document."],

) -> Annotated[str, "Path of the saved document file."]:
    """Create and save a text document."""
    try:
        with (WORKING_DIRECTORY / file_name).open("w") as file:
            file.write(content)
        return f"Document saved to {file_name}"
    except Exception as e:
        logger.error(f"Error in write_document: {str(e)}")
        return f"Error occurred while writing document: {str(e)}"

@tool
def edit_document(

    file_name: Annotated[str, "Path of the document to be edited."],

    inserts: Annotated[

        Dict[int, str],

        "Dictionary where key is the line number (1-indexed) and value is the text to be inserted at that line.",

    ],

) -> Annotated[str, "Path of the edited document file."]:
    """Edit a document by inserting text at specific line numbers."""
    try:
        with (WORKING_DIRECTORY / file_name).open("r") as file:
            lines = file.readlines()
        sorted_inserts = sorted(inserts.items())
        for line_number, text in sorted_inserts:
            if 1 <= line_number <= len(lines) + 1:
                lines.insert(line_number - 1, text + "\n")
            else:
                return f"Error: Line number {line_number} is out of range."
        with (WORKING_DIRECTORY / file_name).open("w") as file:
            file.writelines(lines)
        return f"Document edited and saved to {file_name}"
    except Exception as e:
        logger.error(f"Error in edit_document: {str(e)}")
        return f"Error occurred while editing document: {str(e)}"

# Define the agents and their tools
llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

def create_agent(llm: ChatOpenAI, tools: list, system_prompt: str) -> str:
    """Create a function-calling agent and add it to the graph."""
    system_prompt += """\nWork autonomously according to your specialty, using the tools available to you.

    Do not ask for clarification.

    Your other team members (and other teams) will collaborate with you with their own specialties.

    You are chosen for a reason! You are one of the following team members: {team_members}."""
    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_prompt),
            MessagesPlaceholder(variable_name="messages"),
            MessagesPlaceholder(variable_name="agent_scratchpad"),
        ]
    )
    agent = create_openai_functions_agent(llm, tools, prompt)
    executor = AgentExecutor(agent=agent, tools=tools)
    return executor

def agent_node(state, agent, name):
    try:
        logger.info(f"Starting {name} agent")
        result = agent.invoke(state)
        logger.info(f"{name} agent completed with result: {result}")
        return {"messages": [HumanMessage(content=result["output"], name=name)]}
    except ValidationError as ve:
        logger.error(f"Validation error in {name} agent: {ve}")
        return {"messages": [HumanMessage(content=f"Validation error in {name} agent: {ve}", name=name)]}
    except Exception as e:
        logger.error(f"Error in {name} agent: {e}")
        return {"messages": [HumanMessage(content=f"Error occurred in {name} agent: {e}", name=name)]}

def create_team_supervisor(llm: ChatOpenAI, system_prompt, members) -> str:
    """An LLM-based router."""
    options = ["FINISH"] + members
    function_def = {
        "name": "route",
        "description": "Select the next role.",
        "parameters": {
            "title": "routeSchema",
            "type": "object",
            "properties": {
                "next": {
                    "title": "Next",
                    "anyOf": [
                        {"enum": options},
                    ],
                },
            },
            "required": ["next"],
        },
    }
    system_prompt += "\nEnsure that you direct the workflow to completion. If no progress is being made, or if the task seems complete, choose FINISH."
    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_prompt),
            MessagesPlaceholder(variable_name="messages"),
            ("system", "Given the conversation above, who should act next? Or should we FINISH? Select one of: {options}"),
        ]
    ).partial(options=str(options), team_members=", ".join(members))
    return (
        prompt
        | llm.bind_functions(functions=[function_def], function_call="route")
        | JsonOutputFunctionsParser()
    )

# ResearchTeam graph state
class ResearchTeamState(TypedDict):
    messages: Annotated[List[BaseMessage], operator.add]
    team_members: List[str]
    next: str

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

search_agent = create_agent(
    llm,
    [tavily_tool],
    "You are a research assistant who can search for up-to-date info using the tavily search engine.",
)
search_node = functools.partial(agent_node, agent=search_agent, name="Search")

research_agent = create_agent(
    llm,
    [scrape_webpages],
    "You are a research assistant who can scrape specified urls for more detailed information using the scrape_webpages function.",
)
research_node = functools.partial(agent_node, agent=research_agent, name="WebScraper")

supervisor_agent = create_team_supervisor(
    llm,
    "You are a supervisor tasked with managing a conversation between the"
    " following workers: Search, WebScraper. Given the following user request,"
    " respond with the worker to act next. Each worker will perform a"
    " task and respond with their results and status. When finished,"
    " respond with FINISH.",
    ["Search", "WebScraper"],
)

research_graph = StateGraph(ResearchTeamState)
research_graph.add_node("Search", search_node)
research_graph.add_node("WebScraper", research_node)
research_graph.add_node("supervisor", supervisor_agent)

# Define the control flow
research_graph.add_edge("Search", "supervisor")
research_graph.add_edge("WebScraper", "supervisor")
research_graph.add_conditional_edges(
    "supervisor",
    lambda x: x["next"],
    {"Search": "Search", "WebScraper": "WebScraper", "FINISH": END},
)

research_graph.add_edge(START, "supervisor")
chain = research_graph.compile()

def enter_chain(message: str):
    results = {
        "messages": [HumanMessage(content=message)],
    }
    return results

research_chain = enter_chain | chain

# Document writing team graph state
class DocWritingState(TypedDict):
    messages: Annotated[List[BaseMessage], operator.add]
    team_members: str
    next: str
    current_files: str

def prelude(state):
    written_files = []
    if not WORKING_DIRECTORY.exists():
        WORKING_DIRECTORY.mkdir()
    try:
        written_files = [
            f.relative_to(WORKING_DIRECTORY) for f in WORKING_DIRECTORY.rglob("*")
        ]
    except Exception:
        pass
    if not written_files:
        return {**state, "current_files": "No files written."}
    return {
        **state,
        "current_files": "\nBelow are files your team has written to the directory:\n"
        + "\n".join([f" - {f}" for f in written_files]),
    }

doc_writer_agent = create_agent(
    llm,
    [write_document, edit_document, read_document],
    "You are an expert writing a research document.\n"
    "Below are files currently in your directory:\n{current_files}",
)
context_aware_doc_writer_agent = prelude | doc_writer_agent
doc_writing_node = functools.partial(
    agent_node, agent=context_aware_doc_writer_agent, name="DocWriter"
)

note_taking_agent = create_agent(
    llm,
    [create_outline, read_document],
    "You are an expert senior researcher tasked with writing a paper outline and"
    " taking notes to craft a perfect paper.{current_files}",
)
context_aware_note_taking_agent = prelude | note_taking_agent
note_taking_node = functools.partial(
    agent_node, agent=context_aware_note_taking_agent, name="NoteTaker"
)

chart_generating_agent = create_agent(
    llm,
    [read_document],
    "You are a data viz expert tasked with generating charts for a research project."
    "{current_files}",
)
context_aware_chart_generating_agent = prelude | chart_generating_agent
chart_generating_node = functools.partial(
    agent_node, agent=context_aware_note_taking_agent, name="ChartGenerator"
)

doc_writing_supervisor = create_team_supervisor(
    llm,
    "You are a supervisor tasked with managing a conversation between the"
    " following workers: {team_members}. Given the following user request,"
    " respond with the worker to act next. Each worker will perform a"
    " task and respond with their results and status. When finished,"
    " respond with FINISH.",
    ["DocWriter", "NoteTaker", "ChartGenerator"],
)

authoring_graph = StateGraph(DocWritingState)
authoring_graph.add_node("DocWriter", doc_writing_node)
authoring_graph.add_node("NoteTaker", note_taking_node)
authoring_graph.add_node("ChartGenerator", chart_generating_node)
authoring_graph.add_node("supervisor", doc_writing_supervisor)

authoring_graph.add_edge("DocWriter", "supervisor")
authoring_graph.add_edge("NoteTaker", "supervisor")
authoring_graph.add_edge("ChartGenerator", "supervisor")
authoring_graph.add_conditional_edges(
    "supervisor",
    lambda x: x["next"],
    {
        "DocWriter": "DocWriter",
        "NoteTaker": "NoteTaker",
        "ChartGenerator": "ChartGenerator",
        "FINISH": END,
    },
)

authoring_graph.add_edge(START, "supervisor")
chain = authoring_graph.compile()

def enter_chain(message: str, members: List[str]):
    results = {
        "messages": [HumanMessage(content=message)],
        "team_members": ", ".join(members),
    }
    return results

authoring_chain = (
    functools.partial(enter_chain, members=authoring_graph.nodes)
    | authoring_graph.compile()
)

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

supervisor_node = create_team_supervisor(
    llm,
    "You are a supervisor tasked with managing a conversation between the"
    " following teams: {team_members}. Given the following user request,"
    " respond with the worker to act next. Each worker will perform a"
    " task and respond with their results and status. Make sure each team is used atleast once. When finished,"
    " respond with FINISH.",
    ["ResearchTeam", "PaperWritingTeam"],
)

class State(TypedDict):
    messages: Annotated[List[BaseMessage], operator.add]
    next: str

def get_last_message(state: State) -> str:
    return state["messages"][-1].content

def join_graph(response: dict):
    return {"messages": [response["messages"][-1]]}

super_graph = StateGraph(State)
super_graph.add_node("ResearchTeam", get_last_message | research_chain | join_graph)
super_graph.add_node("PaperWritingTeam", get_last_message | authoring_chain | join_graph)
super_graph.add_node("supervisor", supervisor_node)

super_graph.add_edge("ResearchTeam", "supervisor")
super_graph.add_edge("PaperWritingTeam", "supervisor")
super_graph.add_conditional_edges(
    "supervisor",
    lambda x: x["next"],
    {
        "PaperWritingTeam": "PaperWritingTeam",
        "ResearchTeam": "ResearchTeam",
        "FINISH": END,
    },
)
super_graph.add_edge(START, "supervisor")
super_graph = super_graph.compile()

input_text = st.text_input("Enter your query:")

if input_text and os.getenv("OPENAI_API_KEY") and os.getenv("TAVILY_API_KEY"):
    st.markdown("### 🛠️ Task Progress")
    start_time = time.time()
    max_execution_time = 300  # 5 minutes

    try:
        for s in super_graph.stream(
            {
                "messages": [
                    HumanMessage(
                        content=input_text
                    )
                ],
            },
            {"recursion_limit": 300},  # Increased recursion limit
        ):
            if "__end__" not in s:
                st.write(s)
                st.write("---")
            
            # Check for timeout
            if time.time() - start_time > max_execution_time:
                st.warning("Execution time exceeded. Terminating the process.")
                break
    except RetryError as re:
        st.error(f"Retry error occurred: {re}")
        logger.error(f"Retry error in super_graph execution: {re}")
    except ValidationError as ve:
        st.error(f"Validation error occurred: {ve}")
        logger.error(f"Validation error in super_graph execution: {ve}")
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")
        logger.error(f"Error in super_graph execution: {str(e)}")

if st.button("List Output Files"):
    files = os.listdir(WORKING_DIRECTORY)
    if files:
        st.write("### 📂 Files in working directory:")
        for file in files:
            st.write(f"📄 {file}")
    else:
        st.write("No files found in the working directory.")

output_files = os.listdir(WORKING_DIRECTORY)
if output_files:
    output_file = st.selectbox("Select an output file to download:", output_files)

    if st.button("Download Output Document"):
        file_path = WORKING_DIRECTORY / output_file
        if file_path.exists():
            with file_path.open("rb") as file:
                st.download_button(
                    label="📥 Download Output Document",
                    data=file,
                    file_name=output_file,
                )
        else:
            st.write("Output document not found.")
else:
    st.write("No output files available for download.")

# Cleanup
if st.button("Clear Working Directory"):
    for file in WORKING_DIRECTORY.iterdir():
        if file.is_file():
            file.unlink()
    st.success("Working directory cleared.")