Visual Quest

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  Game Play

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  Main Menu

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  Game play

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Inspiration

The inspiration for Visual Quest came from a love for word games and visual puzzles. I wanted to create a game that not only entertains but also challenges players to think critically and expand their vocabulary. By combining beautiful imagery with clever wordplay, I aimed to develop an addictive and educational experience that appeals to players of all ages. My goal was to create a game that brings joy and learning together, making it a perfect pastime for anyone looking to sharpen their mind while having fun.

What it does

Visual Quest is a word-guessing game that presents players with four different images, all of which share a common theme. Players must identify the hidden word that connects these images. The game features hundreds of levels, each with unique picture collages. It offers hints and the ability to shuffle letters to assist players when they get stuck. Additionally, players can select different areas of interest such as "Travel and Adventure," "Sports and Fitness," "Arts and Entertainment," "Health and Wellness," and "School and Learning" to tailor the game to their preferences.

How I built it

Visual Quest was built using Python, with Streamlit used to create the front-end interface. I used Snowflake Arctic, an efficient and open-source foundational LLM, to generate the words and hints for each puzzle. Arctic excels at following instructions and outputs the required data in the exact format needed. For image generation, I used OpenAI’s API to create visuals based on the words generated by Arctic. I then mixed random letters with the correct word to create challenging puzzles for the players, allowing them to rearrange the letters to form the correct word.

Challenges I ran into

During the development of Visual Quest, I faced several significant challenges. Firstly, it had been a long time since I last used the Python programming language, so I had to quickly reacquaint myself with it within a very short time frame. This required a steep learning curve and efficient time management to ensure the project stayed on track.

Secondly, Creating a user-friendly interface presented a significant challenge. Streamlit, although well-suited for building interactive web applications, isn’t commonly used for developing game interfaces. Overcoming this limitation demanded creative thinking and innovative solutions to design a functional and visually appealing user interface. Balancing the requirements of a game UI with Streamlit’s constraints involved extensive trial and error, resulting in a distinctive and effective interface for Visual Quest.

Accomplishments that I'm proud of

During the development of Visual Quest, I achieved several significant milestones:

Relearned Python and completed the project within a remarkably short timeframe. Adapting swiftly to the language was a personal triumph.

Efficiently fine-tuned Snowflake Arctic to handle word and hint generation. This optimization significantly improved the game experience.

The positive feedback from users who tested the game has been immensely rewarding. Their validation underscores our hard work and dedication.

What I learned

Throughout the development of Visual Quest, I gained valuable insights into several key areas. One significant learning was the versatility of the Arctic LLM. Initially known for its capabilities in coding and following instructions, I discovered that Arctic is also highly effective for generating words within specific areas of interest. This functionality proved invaluable for this word game, allowing me to create relevant and engaging content for players.

What's next for Visual Quest

The future of Visual Quest is bright, with several exciting features and improvements on the horizon:

• Expanding the range of areas of interest and adding more diverse and challenging levels
• Implementing daily challenges and rewards to keep the gameplay fresh and engaging
• Adding high scores to enhance competitiveness
• Making the game available on popular platforms
• Continuous updates and enhancements based on player feedback

Built With

• arctic
• openai
• python
• snowflake
• streamlit-ui