Converts spoken input into text, translates the response, and provides the response as translated audio. Begins with Whisper Speech to Text to transcribe audio input, uses OpenAI GPT Text Generator to create a textual response, converts the text back to speech with OpenAI Text to Speech, and uploads the audio file using Buildship GCP Storage Upload Base64. Returns the stored audio file URL.
Select the reason for reporting
Describe the issue in detail
The Focus Group simulator synthesizes AI-generated insights for focus group discussions to support preliminary research. It takes participant input and queries various AI models, including Perplexity, OpenAI, Diffbot, DeepSeek, xAI Grok, Anthropic Claude, Groq, and Gemini, in parallel to gather a range of perspectives from their knowledge bases. The workflow processes these inputs into a simulated report.
Converts or processes files and uploads them to cloud storage based on their type, producing a final output link. Determines if the file is Markdown; if true, converts it using a Markdown Converter. Otherwise, uploads the file as base64, applies Mistral AI OCR for text extraction, generates a UUID, and uploads the text to cloud storage, seamlessly managing different file types and storage operations.
Executes a conversational AI interaction using Perplexity AI Chat and provides a consolidated output. Initiates a chat session with Perplexity AI, processes the conversation, and delivers the final response as the flow output.
Generates a task breakdown and logs it into Google Sheets with the current date and time. Initializes variables, retrieves the current date and time, uses OpenAI to break down a task, loops through the steps to add each as a row in Google Sheets, and outputs the flow. Leverages OpenAI for task breakdown and Google Sheets API for data logging.
Generates insights by engaging in AI-driven conversations on a given topic using current date and time as context. Utilizes Perplexity AI and OpenAI to simulate interactive chats based on the topic and contextual time data, then consolidates the responses into a unified output.
Converts audio input into text using Whisper Speech-to-Text, then outputs the transcribed text. Initiates with an audio file, employs Whisper to transcribe the spoken words, and then returns the text output for further use or analysis.
