Image-to-Speech-GenAI-Tool-Using-LLM

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Image-to-Speech-GenAI-Tool-Using-LLM

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AI tool that generates an Audio short story based on the context of an uploaded image by prompting a GenAI LLM model, Hugging Face AI models together with OpenAI & LangChain

Image-to-Speech-GenAI-Tool-Using-LLM 최신버젼 다운로드

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# 🖼️Image to Speech GenAI Tool Using LLM 🌟♨️
AI tool that generates an Audio short story based on the context of an uploaded image by prompting a GenAI LLM model, Hugging Face AI models together with OpenAI &amp; LangChain. Deployed on Streamlit & Hugging Space Cloud Separately.

## 📢Run App with Streamlit Cloud

[Launch App On Streamlit](https://image-to-speech-genai-tool-using-llm.streamlit.app/)

## 📢Run App with HuggingFace Space Cloud

[Launch App On HuggingFace Space](https://huggingface.co/spaces/GurpreetKJ/Image-to-SpeechStory_GenAI-Tool)

## 🎯 Demo:
![Demo 1: Couple Test Image Output](https://raw.githubusercontent.com/GURPREETKAURJETHRA/Image-to-Speech-GenAI-Tool-Using-LLM/main/img-audio/CoupleOutput.jpg)

You can listen respective audio file of this test demo images on respective `img-audio` folder

## 📈System Design

![system-design](https://raw.githubusercontent.com/GURPREETKAURJETHRA/Image-to-Speech-GenAI-Tool-Using-LLM/main/img/system-design.drawio.png)

## 🏆Approach
An app that uses Hugging Face AI models to generate text from an image, which then generates audio from the text.

Execution is divided into 3 parts:
- **Image to text:**
  an image-to-text transformer model ([Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base)) is used to generate a text scenario based on the on the AI understanding of the image context
- **Text to story:**
  OpenAI LLM model is prompted to create a short story (50 words: can be adjusted as reqd.) based on the generated scenario. [gpt-3.5-turbo](https://platform.openai.com/docs/models/gpt-3-5)
- **Story to speech:**
  a text-to-speech transformer model ([espnet/kan-bayashi_ljspeech_vits](https://huggingface.co/espnet/kan-bayashi_ljspeech_vits)) is used to convert the generated short story into a voice-narrated audio file
- A user interface is built using streamlit to enable uploading the image and playing the audio file

![Demo 3: Family Test Image Output](https://raw.githubusercontent.com/GURPREETKAURJETHRA/Image-to-Speech-GenAI-Tool-Using-LLM/main/img-audio/FamilyOutput.jpg)
You can listen respective audio file of this test image on respective `img-audio` folder

## 🌟Requirements

- os
- python-dotenv
- transformers
- torch
- langchain
- openai
- requests
- streamlit

## 🚀Usage

- Before using the app, the user should have personal tokens for Hugging Face and Open AI
- The user should set venv environment and install ipykernel library for running app on local system ide.
- The user should save the personal tokens in an ".env" file within the package as string objects under object names: HUGGINGFACE_TOKEN and OPENAI_TOKEN
- The user can then run the app using the command: streamlit run app.py
- Once the app is running on streamlit, the user can upload the target image
- Execution will start automatically and it may take a few minutes to complete
- Once completed, the app will display:
  - The scenario text generated by the image-to-text transformer HuggingFace model
  - The short story generated by prompting the OpenAI LLM
  - The audio file narrating the short story generated by the text-to-speech transformer model
- Deployed Gen AI App on streamlit cloud and Hugging Space

![Demo 2: Picnic Vaction Test Image Output](https://raw.githubusercontent.com/GURPREETKAURJETHRA/Image-to-Speech-GenAI-Tool-Using-LLM/main/img-audio/PicnicOutput.jpg)

## ▶️Installation

Clone the repository:

`git clone https://github.com/GURPREETKAURJETHRA/Image-to-Speech-GenAI-Tool-Using-LLM.git`

Install the required Python packages:

`pip install -r requirements.txt`

Set up your OpenAI API key & Hugging Face Token by creating a .env file in the root directory of the project with the following contents:

`OPENAI_API_KEY=<your-api-key-here>`
`HUGGINGFACE_API_TOKEN=<<your-access-token-here>`

Run the Streamlit app:

`streamlit run app.py`

## ©️ License

Distributed under the MIT License. See `LICENSE` for more information.

---

#### **If you like this LLM Project do drop ⭐ to this repo and Contributions are welcome! If you have any suggestions for improving this AI Img-Speech Converter, please submit a pull request.💁**
#### Follow me on [![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/gurpreetkaurjethra/) &nbsp; [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/GURPREETKAURJETHRA/)

---

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