TrendTok

Fed up with the same TikTok content? Struggling for fresh trend ideas? Introducing AI-powered trend creation! Generate novel trends and give influencers ready-to-use templates.

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Problem Statement: Inspiring creativity with Generative AI

✍️Team Biography

🌟 Inspiration

In a world increasingly influenced by social media trends and influencers, we’ve noticed that developing new, engaging trends is becoming more difficult. This challenge spans all categories of short videos. We believe that using Generative AI (GenAI) to ideate new trends based on inspiration from existing ones can significantly diversify content and better engage audiences.

💼 What It Does

Our app analyzes trending TikTok videos and generates new video ideas in the form of short descriptions, along with novel AI-generated music tracks to serve as background scores and image generation features, providing visual templates to further enhance the creative process. This combination helps creators develop new and engaging content.

🔨 How We Built It

We used a multi-step process involving several advanced technologies to build our AI-driven music synthesis and trend generation app.

  1. Data Collection:

    • Web Scraping: We employed tools like Selenium and BeautifulSoup to scrape trending videos from TikTok. Selenium allowed us to automate web browsing and interaction, while BeautifulSoup enabled us to parse the HTML content and extract relevant data such as video descriptions, hashtags, and engagement metrics.
    • API Integration: In cases where web scraping was limited, we used third-party APIs to supplement our data collection, ensuring we had a comprehensive dataset of trending videos.

  2. Data Summarization:

    • Natural Language Processing (NLP): We utilized Python libraries such as Speech Recognition, Moviepy Editor, and Pytesseract (OCR) to process the text data extracted from the videos. These extracted texts are then summarized and tags are generated.
    • Summarization and Tags Generation: We used the Llama 3 API to summarize the video descriptions into concise summaries that captured the essence of the trending content. We also generate relevant tags from the summary for idea generation.

  3. Idea Generation:

    • Llama 3 Large Language Model (LLM): We fed the summarized data into a Llama 3 LLM. This model, known for its advanced natural language understanding capabilities, generated novel combinations of ideas based on the input summaries. The model created short descriptions of new video ideas that were inspired by the existing trends but presented in a fresh and engaging way.

  4. Audio Cue Generation:

    • Meta MusicGen Model: To generate background music tracks, we used the open-source Meta MusicGen model. This model converts text descriptions (audio cues) into music. We fed the textual audio cues generated by the LLM into this model, which then produced corresponding AI-generated music tracks.

  5. Image Generation:

    • Stable Diffusion Model: To generate unique and engaging images, we used the Stable Diffusion V1.5 . This model creates high-quality images based on text descriptions, providing visual elements to complement the generated video ideas and music tracks.

  6. Integration into a Web App:

    • Frontend Development: We built the frontend using HTML, Bootstrap, and JavaScript to create interactive and user-friendly interfaces.
    • Backend Development: Flask powered the backend to handle server-side operations and API requests.
    • Server and Hosting: We utilized Google Cloud Platform(GCP) for hosting the frontend and managing user data. This setup ensured fast loading times and reliable accessibility.
    • Security and Scaling: The entire application is containerized using Docker, providing encapsulation and security for identifiable data transmitted over the internet. This containerization, combined with deployment on Kubernetes, ensures scalability to accommodate a larger audience.

🌍 Market Impact

TrendTok is set to transform the creative industry! By harnessing AI's endless creativity, creators can keep their content fresh and engaging, leading to endless entertainment for users and maximized engagement for creators. This innovation enhances content diversity and richness on TikTok, driving higher user engagement and retention. The increased activity benefits creators through greater exposure and monetization opportunities, while solidifying TikTok's position as a leading social media platform. TrendTok’s approach sets new standards for creativity and user interaction, leading to greater advertising revenue and robust community interactions. By revolutionizing content creation and consumption, TrendTok is shaping the future of digital entertainment.

🚀 Challenges We Faced

  • Data Access: Scraping TikTok data required access to APIs only available to doctoral researchers, so we explored alternative methods. We used web scraping, third-party APIs, social media monitoring tools, public data repositories, data donations from TikTok users, and manual data collection.
  • Model Availability: Obtaining open-source GenAI models was challenging due to the limited availability of music & image generator models and free LLMs suitable for cloud-based API calls.
  • Compute Power: The high compute power required for video and image generation models necessitated using lighter models and cloud-based APIs.

🏆 Accomplishments

We are incredibly proud of the groundbreaking achievements we accomplished with our project:

  1. Revolutionizing Trend Generation: We have pioneered a sophisticated AI-driven pipeline that generates not only textual ideas but also original audio tracks and image visualization templates, enabling creators to develop unique and engaging content that stands out in the crowded TikTok landscape.

  2. Cutting-Edge AI Integration: Our application seamlessly integrates state-of-the-art AI technologies, including Llama 3 LLM for idea generation, Meta MusicGen for music synthesis and StableDiffusion for image visualization templates, showcasing our ability to harness advanced models for practical, creative solutions.

  3. Enhanced User Engagement: By providing influencers with ready-to-use templates for new trends, we empower them to captivate their audiences with fresh, innovative content, thus driving higher levels of engagement and interaction on the platform.

  4. Robust Data Processing: We successfully implemented a comprehensive data collection and processing framework, utilizing web scraping and NLP techniques to extract and distill relevant information from trending videos, ensuring our AI models have the best possible data to work with.

  5. Scalable and Secure Infrastructure: Our solution is deployed in a Docker container, providing a degree of encapsulation and security for identifiable data which is sent over the internet. We also intend to deploy it in a Kubernetes instance to scale our app for use by a much bigger audience.

🧠 What We Learned

We learned to deploy cutting-edge Generative AI models in text, video, audio, and image generation. We experienced firsthand the challenges of the compute power needed for inferencing large models for image and video generation. Additionally, we gained valuable insights into integrating various technologies seamlessly to create a cohesive and functional product.

🚀 What's Next

We plan to test our model on historical trend data to measure its accuracy in predicting future trends, thereby demonstrating the pipeline's robustness. Given the extended inference times for large models on PCs, we will utilize a dedicated cloud machine for these tasks. Due to limitations in collecting real-time data from TikTok, we plan to integrate this feature in the future by obtaining the necessary permissions. Furthermore, we plan to explore partnerships with content creators and influencers to refine our application and expand its reach within the TikTok community.

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