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----
-title: VacAIgent
-emoji: 🐨
-colorFrom: yellow
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Let AI agents plan your next vacation!
----
-
-# 🏖️ VacAIgent: Streamlit-Integrated AI Crew for Trip Planning
-
-_Forked and enhanced from the_ [_crewAI examples repository_](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
-
-![Beach Vacation Scene ~ generated by GPT-4V](images/beach.png)
-
-## Introduction
-
-VacAIgent leverages the CrewAI framework to automate and enhance the trip planning experience, integrating a user-friendly Streamlit interface. This project demonstrates how autonomous AI agents can collaborate and execute complex tasks efficiently, now with an added layer of interactivity and accessibility through Streamlit.
-
-**Check out the video below for code walkthrough** 👇
-
-<a href="https://youtu.be/nKG_kbQUDDE">
-  <img src="https://img.youtube.com/vi/nKG_kbQUDDE/hqdefault.jpg" alt="Watch the video" width="100%">
-</a>
-
-(_Trip example originally developed by [@joaomdmoura](https://x.com/joaomdmoura)_)
-
-## CrewAI Framework
-
-CrewAI simplifies the orchestration of role-playing AI agents. In VacAIgent, these agents collaboratively decide on cities and craft a complete itinerary for your trip based on specified preferences, all accessible via a streamlined Streamlit user interface.
-
-## Streamlit Interface
-
-The introduction of [Streamlit](https://streamlit.io/) transforms this application into an interactive web app, allowing users to easily input their preferences and receive tailored travel plans.
-
-## Running the Application
-
-To experience the VacAIgent app:
-
-### Pre-Requisites
-1. Install and Configure **git** on your machine
-2. Get the API key from **scrapinagent.com** from  scrapinagent [Click Here to Signup](https://scrapingant.com/)
-3. Get the API from **SERPER API** from serper [Click here to Signup]( https://serper.dev/)
-
-### Deploy Trip Planner
-
-####  Step 1
-```sh
-git clone https://github.com/intel-sandbox/trip_planner_agent
-```
-* *Please make sure git is installed*
-
-#### Step 2
-
-Insall Dependencies
-```sh
-pip install -r requirements.txt
-```
-####  Step 3
-
-```sh
-cd trip_planner_agent
-```
-
-create `.streamlit/secrets.toml` file and Update **Credentials**
- you can use secrtes.example file for reference 
-
-```sh
-SERPER_API_KEY=""
-SCRAPINGANT_API_KEY=""
-OPENAI_API_KEY=""
-MODEL_ID=""
-MODEL_BASE_URL=""
-```
-#### Step 4
-
-Run the application
-
-```sh
-streamlit run streamlit_app.py
-```
-
-Your application should be up and running
-
-★ **Disclaimer**: The application uses GPT-4 by default. Ensure you have access to OpenAI's API and be aware of the associated costs.
-
-## Details & Explanation
-
-- **Streamlit UI**: The Streamlit interface is implemented in `streamlit_app.py`, where users can input their trip details.
-- **Components**:
-  - `./trip_tasks.py`: Contains task prompts for the agents.
-  - `./trip_agents.py`: Manages the creation of agents.
-  - `./tools directory`: Houses tool classes used by agents.
-  - `./streamlit_app.py`: The heart of the Streamlit app.
-
-## Using GPT 3.5
-
-To switch from GPT-4 to GPT-3.5, pass the llm argument in the agent constructor:
-
-```python
-from langchain.chat_models import ChatOpenAI
-
-llm = ChatOpenAI(model='gpt-3.5-turbo') # Loading gpt-3.5-turbo (see more OpenAI models at https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4)
-
-class TripAgents:
-    # ... existing methods
-
-    def local_expert(self):
-        return Agent(
-            role='Local Expert',
-            goal='Provide insights about the selected city',
-            tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
-            llm=llm,
-            verbose=True
-        )
-
-```
-
-## Using Local Models with Ollama
-
-For enhanced privacy and customization, you can integrate local models like Ollama:
-
-### Setting Up Ollama
-
-- **Installation**: Follow Ollama's guide for installation.
-- **Configuration**: Customize the model as per your requirements.
-
-### Integrating Ollama with CrewAI
-
-Pass the Ollama model to agents in the CrewAI framework:
-
-```python
-from langchain.llms import Ollama
-
-ollama_model = Ollama(model="agent")
-
-class TripAgents:
-    # ... existing methods
-
-    def local_expert(self):
-        return Agent(
-            role='Local Expert',
-            tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
-            llm=ollama_model,
-            verbose=True
-        )
-
-```
-
-## Benefits of Local Models
-
-- **Privacy**: Process sensitive data in-house.
-- **Customization**: Tailor models to fit specific needs.
-- **Performance**: Potentially faster responses with on-premises models.
-
-## License
-
-VacAIgent is open-sourced under the MIT License.
-
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference