add gift lfs tracking for images

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+images/hf_vacaigent.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -7,17 +7,18 @@ sdk: streamlit
 sdk_version: 1.44.1
 app_file: app.py
 pinned: false
-license: apache-2.0
+license: mit
 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)
+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.
 
-## Introduction
+_Forked and enhanced from the_ [_crewAI examples repository_](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner). You can find the application hosted on Hugging Face Spaces here:
+
+[![](images/hf_vacaigent.png)](https://huggingface.co/spaces/Intel/vacaigent)
 
-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** 👇
 
@@ -29,28 +30,25 @@ VacAIgent leverages the CrewAI framework to automate and enhance the trip planni
 
 ## 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
+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 Streamlit user 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/)
+1. Get the API key from **scrapinagent.com** from  [scrapinagent](https://scrapingant.com/)
+2. Get the API from **SERPER API** from [serper]( https://serper.dev/)
+3. Bring your OpenAI compatible API key
+4. Bring your model endpoint URL and LLM model ID that you want to use
 
 ### Deploy Trip Planner
 
 ####  Step 1
 ```sh
-git clone https://github.com/intel-sandbox/trip_planner_agent
+git clone https://github.com/opea-project/Enterprise-Inference/
 ```
-* *Please make sure git is installed*
 
 #### Step 2
 
@@ -59,9 +57,10 @@ Insall Dependencies
 pip install -r requirements.txt
 ```
 ####  Step 3
+Add Streamlit secrets
 
 ```sh
-cd trip_planner_agent
+cd examples/trip_planner_agent
 ```
 
 create `.streamlit/secrets.toml` file and Update **Credentials**
@@ -71,52 +70,35 @@ create `.streamlit/secrets.toml` file and Update **Credentials**
 SERPER_API_KEY=""
 SCRAPINGANT_API_KEY=""
 OPENAI_API_KEY=""
-MODEL_ID=""
-MODEL_BASE_URL=""
+MODEL_ID="meta-llama/Llama-3.3-70B-Instruct"
+MODEL_BASE_URL="https://api.inference.denvrdata.com/v1/"
+
 ```
+**Note**: You can alternatively add these secrets directly to Hugging Face Spaces Secrets, under the Settings tab, if deploying the Streamlit application directly on Hugging Face.
+
 #### Step 4
 
 Run the application
 
 ```sh
-streamlit run streamlit_app.py
+streamlit run app.py
 ```
 
-Your application should be up and running
+Your application should be up and running in your web browser.
 
-★ **Disclaimer**: The application uses GPT-4 by default. Ensure you have access to OpenAI's API and be aware of the associated costs.
+★ **Disclaimer**: The application uses meta-llama/Llama-3.3-70B-Instruct by default. Ensure you have access to an OpenAI-compatible API and be aware of any 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
+  - [trip_tasks.py](trip_tasks.py): Contains task prompts for the agents.
+  - [trip_agents.py](trip_agents.py): Manages the creation of agents.
+  - [tools](tools) directory: Houses tool classes used by agents.
+  - [app.py](app.py): The heart of the frontend Streamlit app.
 
-    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
-        )
+## LLM Model
 
-```
+To switch the LLM model being used, you can switch the `MODEL_ID` in the `.streamlit/secrets.toml` file.
 
 ## Using Local Models with Ollama
 
@@ -157,8 +139,4 @@ class TripAgents:
 
 ## License
 
-VacAIgent is open-sourced under the MIT License.
-
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+VacAIgent is open-sourced under the MIT license.