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Community Usecase Bipul Project (#390)
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‎community_usecase/PHI_Sanitization_Summarization_and_Article_Writing/project.py‎

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import streamlit as st
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from dotenv import load_dotenv
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from pathlib import Path
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import os
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# Import Camel-AI and OWL modules
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from camel.models import ModelFactory
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from camel.types import ModelPlatformType, ModelType
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from camel.logger import set_log_level
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from camel.societies import RolePlaying
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from camel.toolkits import (
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ExcelToolkit,
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SearchToolkit,
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CodeExecutionToolkit,
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)
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from owl.utils import run_society
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from owl.utils import DocumentProcessingToolkit
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# Set log level to see detailed logs (optional)
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set_log_level("DEBUG")
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# Load environment variables from .env file if available
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load_dotenv()
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def construct_society(question: str) -> RolePlaying:
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r"""Construct a society of agents based on the given question.
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Args:
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question (str): The task or question to be addressed by the society.
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Returns:
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RolePlaying: A configured society of agents ready to address the question.
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"""
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# Create models for different components
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models = {
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"user": ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI,
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model_type=ModelType.GPT_4O,
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model_config_dict={"temperature": 0},
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),
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"assistant": ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI,
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model_type=ModelType.GPT_4O,
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model_config_dict={"temperature": 0},
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),
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}
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# Configure toolkits
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tools = [
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*CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
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SearchToolkit().search_duckduckgo,
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SearchToolkit().search_wiki,
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SearchToolkit().search_baidu,
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*ExcelToolkit().get_tools(),
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]
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# Configure agent roles and parameters
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user_agent_kwargs = {"model": models["user"]}
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assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
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# Configure task parameters
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task_kwargs = {
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"task_prompt": question,
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"with_task_specify": False,
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}
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# Create and return the society
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society = RolePlaying(
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**task_kwargs,
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user_role_name="user",
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user_agent_kwargs=user_agent_kwargs,
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assistant_role_name="assistant",
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assistant_agent_kwargs=assistant_agent_kwargs,
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)
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return society
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def summarize_section():
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st.header("Summarize Medical Text")
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text = st.text_area("Enter medical text to summarize:", height=200)
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if st.button("Summarize"):
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if text:
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# Create a task prompt for summarization
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task_prompt = f"Summarize the following medical text:\n\n{text}"
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society = construct_society(task_prompt)
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with st.spinner("Running summarization society..."):
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answer, chat_history, token_count = run_society(society)
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st.subheader("Summary:")
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st.write(answer)
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st.write(chat_history)
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else:
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st.warning("Please enter some text to summarize.")
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def write_and_refine_article_section():
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st.header("Write and Refine Research Article")
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topic = st.text_input("Enter the topic for the research article:")
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outline = st.text_area("Enter an outline (optional):", height=150)
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if st.button("Write and Refine Article"):
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if topic:
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# Create a task prompt for article writing and refinement
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task_prompt = f"Write a research article on the topic: {topic}."
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if outline.strip():
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task_prompt += f" Use the following outline as guidance:\n{outline}"
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society = construct_society(task_prompt)
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with st.spinner("Running research article society..."):
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print(task_prompt)
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answer, chat_history, token_count = run_society(society)
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st.subheader("Article:")
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st.write(answer)
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st.write(chat_history)
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else:
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st.warning("Please enter a topic for the research article.")
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def sanitize_data_section():
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st.header("Sanitize Medical Data (PHI)")
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data = st.text_area("Enter medical data to sanitize:", height=200)
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if st.button("Sanitize Data"):
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if data:
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# Create a task prompt for data sanitization
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task_prompt = f"Sanitize the following medical data by removing any protected health information (PHI):\n\n{data}"
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society = construct_society(task_prompt)
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with st.spinner("Running data sanitization society..."):
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answer, chat_history, token_count = run_society(society)
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st.subheader("Sanitized Data:")
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st.write(answer)
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st.write(chat_history)
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else:
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st.warning("Please enter medical data to sanitize.")
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def main():
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st.set_page_config(page_title="Multi-Agent AI System with Camel & OWL", layout="wide")
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st.title("Multi-Agent AI System with Camel-AI and OWL")
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st.sidebar.title("Select Task")
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task = st.sidebar.selectbox("Choose a task:", [
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"Summarize Medical Text",
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"Write and Refine Research Article",
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"Sanitize Medical Data (PHI)"
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])
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if task == "Summarize Medical Text":
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summarize_section()
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elif task == "Write and Refine Research Article":
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write_and_refine_article_section()
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elif task == "Sanitize Medical Data (PHI)":
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sanitize_data_section()
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if __name__ == "__main__":
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main()

‎community_usecase/PHI_Sanitization_Summarization_and_Article_Writing/readme.md‎

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# 🚀 Collaborative Multi-Agent AI System
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Welcome to my latest project: a **multi-agent AI platform** that automates complex tasks through teamwork! This system combines the power of **CAMEL-AI**, **OWL**, and **Streamlit** to create a seamless, interactive experience for task automation and collaboration.
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---
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## ✨ Features
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- **🤖 Multi-Agent Teamwork**: CAMEL-AI + OWL frameworks enable real-time collaboration between autonomous agents.
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- **💡 Autonomous Agents**: Agents communicate, collaborate, and validate outputs for accurate results.
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- **🔗 Seamless Integration**: CAMEL-AI for agent design + OWL for real-time task management.
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- **🌐 Streamlit UI**: A clean, interactive app for easy task execution.
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- **🚀 Use Cases**:
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- Summarize medical texts in seconds.
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- Automate research article generation.
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- Sanitize PHI data for compliance.
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---
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## 🛠️ How It Works
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1. **Agent Roles**: Defined using CAMEL-AI's `RolePlaying` class.
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2. **Dynamic Toolkits**: Integrated CAMEL-AI's tools for agent functionality.
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3. **Real-Time Management**: OWL framework ensures smooth task execution.
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4. **User-Friendly Interface**: Streamlit provides an intuitive UI for users.
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---
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## 🚀 Getting Started
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1. **Clone the repository**:
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```bash
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git clone https://github.com/Bipul70701/Multi-Agent-System-OWL.git
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cd Multi-Agent-System-OWL
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```
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2. **Create a virtual environment**:
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```bash
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python -m venv venv
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```
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3. **Activate the virtual environment**:
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- On Windows:
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```bash
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venv\Scripts\activate
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```
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- On macOS/Linux:
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```bash
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source venv/bin/activate
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```
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4. **Install dependencies**:
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```bash
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pip install -r requirements.txt
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```
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5. **Run the Streamlit app**:
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```bash
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streamlit run app.py
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```
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---
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## 🔧 Key Components
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- **CAMEL-AI**: Framework for designing and managing autonomous agents.
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- **OWL**: Real-time task management and collaboration.
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- **Streamlit**: Interactive web app for user interaction.
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---
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## 📂 Project Structure
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```
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Multi-Agent-System-OWL/
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├── multiagentsystem.py # Streamlit application
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├── owl/ # OWL framework and utilities
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│ └── utils/ # Utility functions and helpers
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├── requirements.txt # List of dependencies
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└── README.md # Project documentation
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```
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---
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## 🌟 Try It Yourself
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Check out the project on GitHub:
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🔗 [GitHub Repository](https://github.com/Bipul70701/Multi-Agent-System-OWL)
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---
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## 🙌 Credits
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- **CAMEL-AI**: For the multi-agent framework.
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- **OWL**: For real-time task management.
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- **Streamlit**: For the interactive UI.
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---
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Made with ❤️ by Bipul Kumar Sharma

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