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https://github.com/aastroza/ai-marketing-campaign-generator

An AI-powered marketing campaign generator using OpenAI's gpt-4o-mini, FastAPI for the backend, and Streamlit for the user interface. Containerized for deployment with Modal.
https://github.com/aastroza/ai-marketing-campaign-generator

fastapi modal openai openai-api openai-chatgpt streamlit

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An AI-powered marketing campaign generator using OpenAI's gpt-4o-mini, FastAPI for the backend, and Streamlit for the user interface. Containerized for deployment with Modal.

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README 

        
# AI-Marketing-Campaign-Generator



This repository contains an AI-powered marketing campaign generator. The application uses [OpenAI's GPT-4o-mini model](https://platform.openai.com/docs/models) to generate creative marketing campaign suggestions based on user prompts. The backend is built using [FastAPI](https://fastapi.tiangolo.com/) and interacts with OpenAI's API, while the frontend is a [Streamlit](https://streamlit.io/) application that provides an interactive user interface. The applications are designed for containerized deployment using the [Modal](https://modal.com/) platform. Users simply input their brand name and a short description, and the system will generate a unique marketing campaign for them.



## Notes



This repository hosts a demo project for the "Desarrollo de Producto de Datos" course of the Data Science Master's program at Universidad del Desarrollo. The purpose of this project is to showcase the integration of various cutting-edge technologies including **Modal**, **Streamlit**, **FastAPI**, and **OpenAI** API.



The application is an AI-powered marketing campaign generator. The backend of the application is developed using **FastAPI**, a modern, fast (high-performance), web framework for building APIs with Python. The frontend is a **Streamlit** application that provides a user-friendly interface for users to input their brand name and description and receive AI-generated marketing campaigns.



The entire application is containerized for deployment using the **Modal** platform, demonstrating an end-to-end data product development cycle, from AI model integration to user interface design and deployment.



This demo project serves as a practical example of building and deploying a data product as part of the course. It illustrates the application of the concepts and technologies taught in the program in a real-world scenario.



## Getting Started



Before you begin to run this project, there are a few prerequisites you will need to have in place:



✅ **OpenAI API Key:** In order to interact with OpenAI's API, you will need to have an API key. You can obtain this by creating an account on OpenAI's website and following their [instructions to generate an API key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key).



✅ **Modal Account:** The application is containerized for deployment using the Modal platform. Visit the [Modal](https://modal.com/signup) website to sign up for an account if you don't have one already.



✅ **Streamlit Account (Optional):** While you can run Streamlit apps locally without an account, having a Streamlit account allows you to deploy and share your apps, which can be useful for demonstrating your project to others. If you wish to use this feature, [sign up for a Streamlit account](https://share.streamlit.io/signup).



## Installation



```

conda create --name ai-marketing-campaign-generator -c conda-forge python=3.10

conda activate ai-marketing-campaign-generator

pip install -r requirements.txt

```



## [SERVER] Modal deployment



Getting started

The nicest thing about all of this is that you don’t have to set up any infrastructure. Just:



- Create an account at modal.com

- Install the modal-client package

- Set up a token using `modal token new`



…and you can start running jobs right away.



```

modal serve .\backend\fastapi_app.py

```



To deploy this as a permanent app, run the command



```

modal deploy .\backend\fastapi_app.py

```



## Updating the Endpoint URL



After deploying your FastAPI application on Modal, you will receive a unique URL for your application. This URL is the endpoint that the Streamlit application will send requests to.



In the `st_app.py` file, you'll find a line of code that defines the endpoint URL:



```python

endpoint = "https://aastroza--example-fastapi-app-fastapi-app.modal.run/campaign"

```



You must replace this URL with the URL you received after your Modal deployment. This ensures that the Streamlit application communicates with your deployed FastAPI application correctly.



## [CLIENT] Streamlit app



```

streamlit run st_app.py

```