

Storytelling Chatbot
Overview :
This product utilizes the Gemini 2.0 language model and Google Imagen image generation technology, integrating speech recognition and synthesis to provide users with an interactive storytelling experience. Users can choose the direction of the story through voice input, and the system will generate story content and related images in real-time. Its main advantages are innovative interaction methods and powerful content generation capabilities, making it suitable for education, entertainment, and creative inspiration. Currently, the product is in the open-source phase, with no specific pricing established, primarily targeting developers and educational institutions.
Target Users :
This product is ideal for educators to develop interactive teaching content, creative professionals seeking inspiration, and everyday users looking for entertaining experiences.
Use Cases
Educational scenario: Teachers use the chatbot to design interactive story lessons that spark students' interest in learning.
Creative writing: Writers interact with the chatbot to gather story ideas and explore various plot developments.
Family entertainment: Parents and children use the product together to co-create their own adventure stories.
Features
Utilizes Deepgram for speech-to-text conversion, transforming user voice commands into text.
Generates story content via Google Gemini 2.0, offering rich narratives and choices.
Uses ElevenLabs to convert text to speech, enhancing the immersive storytelling experience.
Employs Google Imagen to generate images related to the story content, elevating the visual experience.
Supports customizable environment variables, allowing users to adjust settings as needed.
Offers both local running and Docker deployment options for easy development and testing.
How to Use
1. Clone the project code to your local machine.
2. Install the Python environment and create a virtual environment, then run `pip install -r requirements.txt` to install dependencies.
3. Create a `.env` file and configure the relevant environment variables.
4. Navigate to the `frontend` folder, run `npm install` and `npm run build` to build the front end.
5. Start the backend service by running `python src/bot_runner.py --host localhost`.
6. Access `http://localhost:7860` in your browser to start using the chatbot.
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