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ConsumeWise-Health&Product Analysis WhatsApp Bot

ConsumeWise-Health&Product Analysis WhatsApp Bot

Instant Health Insights, Just A Text Away!

9
Built at Gen AI Exchange Hackathon by Google

Created on 2nd October 2024

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ConsumeWise-Health&Product Analysis WhatsApp Bot

ConsumeWise-Health&Product Analysis WhatsApp Bot

Instant Health Insights, Just A Text Away!

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Describe your project

  1. In Scope of the Solution
    User Health Input: Users can input their health conditions (e.g., diabetes) and allergies to receive tailored product recommendations.
    Barcode Scanning: Users can input the barcode of food products for analysis.
    Personalized Health Analysis: The chatbot provides:
    Suitability score for the product based on user health conditions.
    Identification of harmful ingredients in the product.
    Processing level of the food product (e.g., unprocessed, processed).
    Detection of misleading ingredients that may not be beneficial.
    Healthier Alternatives: Users can request healthier alternatives based on the input barcode, providing recommendations for better food choices.
    Integration with Open Food Facts API: The chatbot leverages the Open Food Facts API to fetch product data, nutritional information, and ingredient lists.
  2. Out of Scope of the Solution
    Comprehensive Medical Advice: The chatbot does not replace professional medical advice or consultation with healthcare providers.
    Real-time Data Updating: The solution does not include features for real-time updates of product information beyond what is available from the Open Food Facts API.
    Advanced Nutritional Tracking: The solution does not provide extensive nutritional tracking or personalized meal planning based on user dietary habits over time.
    Offline Functionality: The chatbot requires an internet connection to access the Open Food Facts API and function effectively.
  3. Future Opportunities for the Solution
    Expanded Health Profiles: Incorporate more health conditions and dietary preferences (e.g., vegan, gluten-free) for more personalized recommendations.
    User Feedback Loop: Allow users to provide feedback on recommendations to improve the algorithm's accuracy and relevance.
    Integration with Wearable Devices: Explore the possibility of integrating with health tracking wearables (e.g., fitness trackers) to analyze users' health data in real-time.

Challenges we ran into

Challenges :

  1. Working With Open Food Facts API, Getting Ingredients and Product Categories.

  2. Signing Up With Vertex AI, Problems Getting The OTP For Registration and Debit Card Decline Issues.

  3. The API often lacked complete ingredient lists and NOVA group data, which required us to implement fallback mechanisms using local data to ensure accurate recommendations.

  4. Not all packaged products were available in the API, so we had to rely on fallback data and manual inputs for missing products.

Tracks Applied (1)

4. Problem statement shared by People+ai (ConsumeWise)

N/A

Technologies used

1. Scikit-Learn

2. Python

3. Flask

4. Twilio

5. Ngrok

Discussion

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