Build a No-Code AI App That Turns Video to Text with Gemini 2.5
In the ever-evolving landscape of AI and no-code development, the ability to transform video content into structured, searchable text is a game-changer. This article explores how to build a no-code AI application using Gemini 2.5 integrated with Momen, a full-stack no-code web app development platform. This powerful combination enables developers and creators to analyze videos, extract meaningful information, and automate workflows—all without writing a single line of code.
Gemini 2.5 stands out as a commercially viable large language model capable of handling multimodal inputs, including video, images, and text. This feature opens up exciting possibilities for video analysis, question answering, and content generation. In this guide, you will learn how to leverage Gemini 2.5 within the Momen platform to build robust AI-driven applications that turn videos into insightful text outputs, complete with practical demonstrations and tips for managing AI workflows and user access.
What is Momen and Why Gemini 2.5?
Momen is a no-code platform designed for full-stack web app development. It allows users to build complex applications without writing code, integrating AI agents seamlessly into workflows. Recently, Momen introduced an integration with Gemini 2.5, a large language model that uniquely supports video analysis alongside traditional text and image processing.
Gemini 2.5 is notable for its ability to analyze videos and generate text-based summaries, answer questions about video content, and combine multiple modalities like images, videos, and text in a single query. This capability is rare among commercially available AI models, making Gemini 2.5 a valuable tool for developers and businesses looking to automate video content analysis efficiently.
With Momen’s integration of Gemini 2.5, users can buy AI points directly on the platform and utilize different Gemini models such as Gemini 2.5 Pro and Gemini 2.5 Flash. While the platform currently does not support bringing your own model for Gemini, this feature is expected soon, further expanding customization options.
Demo: Turning Video into Structured Text with Gemini 2.5
One of the most compelling features of using Gemini 2.5 with Momen is the ability to create multimodal question-answering (QA) agents that analyze video and image inputs to produce structured text outputs. Let’s dive into some practical demonstrations to illustrate this capability.
Multimodal Question Answering Agent
The multimodal QA agent processes video and image inputs and answers user queries based on the content. For example, if you upload a video of a safari in Africa, the agent can answer questions such as “How many people with cameras are in the video?” or “What animals are present?”
Through a simple interface, users enter their questions and preferences, and the AI processes the video to generate concise, accurate answers. In one test, the agent correctly identified two people holding cameras in the video, demonstrating the model's ability to interpret complex visual data.
Another impressive feature is the agent’s ability to output answers in markdown format, making the results easy to format and integrate into other documents or web pages. For instance, the AI can list people appearing in a video with descriptions formatted neatly in markdown.
Combining Video and Image Inputs for Richer Outputs
Besides videos, Gemini 2.5 can handle image inputs simultaneously, allowing for even richer content analysis. For example, a video of coding on top of Mount Kilimanjaro combined with an image from a recent panel discussion can be analyzed together. The AI then generates creative marketing material for Momen based on both inputs.
The marketing copy produced is vibrant and engaging, showcasing the power of Gemini to synthesize information across different media. The output can also be tailored to specific formats such as HTML, with styling that complements the content, making it suitable for direct embedding in websites or apps.
Advanced Features: Conditional Views and Structured Output
To enhance user experience and provide clarity, Momen supports conditional views based on the AI’s confidence in answering a question. For example, a question-answering interface can show a green tick if the question is answerable based on the video/image content or a red cross if not, along with a clear "not answerable" message.
This feature is particularly useful in real-world applications like security footage analysis, where questions may or may not be answerable from the given video. For instance, if a user asks about adding an image to a web page, which is unrelated to the video content, the system will clearly indicate that the question cannot be answered.
Security Footage Analysis Use Case
Analyzing security footage is a practical application where this technology shines. Long and tedious hours of video can be automatically summarized, highlighting incidents such as attempted thefts. The AI generates detailed descriptions of events, including timestamps, which can be invaluable for security personnel reviewing footage.
This automation dramatically reduces labor costs and increases efficiency, as the AI handles the initial pass-through of footage, flagging key moments for human review. Moreover, the cost of running such analysis is minimal—approximately 15 to 20 cents per hour of footage using Gemini 2.5 Flash.
Custom UI and Debugging: Designing No-Code Screens with Momen
Momen’s platform provides a visual interface builder that allows developers to design custom UI screens without coding. This includes adding components like HTML containers that can render AI-generated HTML content directly, enabling rich and dynamic responses in the app.
Debugging tools are integrated into the platform, allowing developers to inspect page states, variables, and component properties in real-time. This is particularly useful when fine-tuning AI prompts or managing complex workflows involving multiple inputs and outputs.
HTML Output and Styling Challenges
Generating HTML output from AI responses can be tricky. The AI needs clear instructions to omit tags like <html> and <body> when the content is to be embedded inside an existing page. The styling should reflect the content’s nature but remain consistent with the overall design.
Sometimes the AI may not fully respect these constraints, requiring prompt adjustments or backend updates. However, once configured correctly, the system can produce visually appealing marketing copy, structured reports, or documentation sections in HTML format.
Using YouTube Links and Data Modeling Best Practices
Momen supports uploading videos directly or providing YouTube links for analysis. Though direct YouTube video fetching by Gemini is not yet available, it is on the roadmap. When YouTube links are included, the AI can generate clickable timestamps in the output, allowing users to jump directly to relevant video sections.
This feature enhances navigation and usability, especially for longer videos. For example, a video on data models can be segmented into structured sections with beginning and end timestamps, each linked back to the corresponding part of the video. This creates an interactive, hyperlinked documentation of the video content.
Segmentation and Summarization
The AI breaks down videos into coherent segments, summarizing each with timestamps. This is invaluable for creating outlines, documentation, or study guides based on video content. The markdown output produced can be further customized or transformed into other formats.
Automating Workflows and Managing User Credits
One of the strengths of combining Gemini 2.5 with Momen is the ability to automate entire AI workflows and manage access through a credit system. This means users can be given a certain number of AI credits, which are consumed each time they run a query or analysis.
This setup is ideal for SaaS models or platforms offering AI services where usage needs to be monetized or limited. The credit deduction logic can be implemented as part of the AI workflow, ensuring that credits are only deducted upon successful execution.
Example: Credit-Based Access Control
Create a credit field for each user account.
Set permissions to allow AI queries only if the user has sufficient credits.
Automatically reduce credits by one with each AI query.
Show error messages or disable functionality if credits are insufficient.
This approach balances user experience with monetization, allowing platform owners to control AI usage effectively.
Batch Processing and API Integration
Beyond interactive user input, Momen supports batch processing of videos and images through action flows and custom code. For example, you can create a workflow that accepts a video URL, uploads it to Momen, triggers Gemini analysis, and stores the extracted data in a table—all programmatically.
This enables developers to build scalable solutions that process large volumes of media automatically without manual intervention. The platform’s support for structured output and conditional logic within action flows further enhances automation capabilities.
Cost Efficiency and Practical Considerations
Running video analysis with Gemini 2.5 on Momen is surprisingly cost-effective. Analysis costs for an hour of video are around 20 cents using the Gemini 2.5 Flash model, making it accessible for individual developers and small businesses.
However, it’s important to optimize AI prompts and token limits to balance performance and cost. For example, increasing maximum tokens allows more detailed responses but may increase expenses. Debugging and prompt tuning are essential to achieve optimal results.
Conclusion: Harnessing the Power of Gemini 2.5 and Momen for No-Code AI Apps
The integration of Gemini 2.5 with the Momen no-code platform opens up a world of possibilities for building AI applications that analyze video content and generate structured text outputs. From multimodal question answering to automated security footage analysis and marketing content generation, this technology empowers creators and developers to unlock the value hidden in video data without writing code.
With features like conditional views, HTML output, YouTube link support, workflow automation, and credit-based access control, Momen provides a comprehensive environment for deploying practical AI solutions. The cost-effective nature of Gemini 2.5 Flash further lowers the barrier to entry, making advanced video-to-text AI accessible to a broad audience.
Whether you are developing a SaaS product, creating interactive documentation, or automating tedious video reviews, leveraging Gemini 2.5 on Momen offers a powerful, flexible, and user-friendly path forward. By combining no-code ease with cutting-edge AI capabilities, you can build innovative applications that transform how video content is consumed and utilized.
Explore the possibilities today and start building your own no-code AI apps to turn videos into insightful, actionable text.
