Gut Decode

Inspiration

The inspiration for Microbiome Mentor stems from the growing awareness of the gut microbiome's crucial role in overall health, coupled with the overwhelming complexity and often contradictory information surrounding diet's impact on it. While scientific research is exploding, it's largely locked away in dense papers or scattered across the web, making it incredibly difficult for the average person to understand.

We noticed a specific gap when looking at food choices, especially concerning processed foods and their numerous additives. People are increasingly curious (and often concerned) about ingredients like artificial sweeteners, emulsifiers, or even specific types of fiber, but finding reliable, synthesized information on their potential effects on gut flora is a real challenge.

What it does

Gut Decode uses Perplexity Sonar API to analyze microbiome, diet, and health queries, providing structured scientific insights.

How we built it

We built a Python backend using FastAPI. It sends user queries to the Perplexity API for scientific microbiome/health analysis and structures the returned insights for clear user understanding.

Challenges we ran into

• Optimizing prompts for Perplexity API: Refining queries to ensure Perplexity returned accurate, scientifically focused, and consistent microbiome insights.
• Structuring unstructured data from API responses: Reliably converting Perplexity's natural language answers into a defined, usable, and structured JSON format.
• Applying LLM guardrails for safe and relevant outputs: Implementing safeguards to ensure AI responses were responsible, contextually appropriate, and safe for health-related queries.

Accomplishments that we're proud of

Proudly delivering structured scientific insights on microbiome health by effectively leveraging Sonar Perplexity API, making complex data understandable.

What we learned

• Prompt Engineering: The critical role of precise prompt design in obtaining accurate, relevant, and structured information from an AI.
• Data Transformation: Techniques for converting complex, natural language AI responses into consistently structured, usable data for backend processing.
• Responsible AI in Health: The vital importance of implementing safeguards, ensuring data privacy, and considering ethical implications when providing AI-driven health-related information.

What's next for Gut Decode

• Deeper Personalization & User Profiles: Introducing user accounts to allow individuals to track their queries, receive more tailored insights over time, and build a personal microbiome health journey.

• Direct Microbiome Data Upload: Enabling users to upload their actual microbiome test results (e.g., from sequencing reports) for a more comprehensive and personalized analysis by the Perplexity API.

• Actionable Recommendations: Moving beyond analysis to offer more concrete, evidence-based suggestions for diet and lifestyle adjustments, always with scientific backing and appropriate disclaimers.

Built With

• fastapi
• perplexity
• pydantic
• python