Android AI Photo DetectivAndroid AI Photo Detective وصف المسابقة (Contest description): I'm building a mobile experience that lets a user pick a picture from their gallery and instantly discover who's in it and how they تكملة مقترحة للوصف (بما أن النص
Budget: $25 – $50 USD
I’m building a mobile experience that lets a user pick a picture from their gallery and instantly discover who’s in it and how they feel. Think of Sherlock-style intelligence focused on faces and emotions, but built natively for Android first, with a clean, modern interface that will later scale to iOS.
Here is the flow I need implemented end-to-end. A user opens the app, chooses a photo stored on the device, the image is uploaded securely to the cloud, an AI model analyses faces, detects their emotions, and returns the findings in seconds. Results should appear in an elegant card-based UI that feels at home on recent Material guidelines.
All design work, backend APIs, AI model integration, security (encryption in transit and at rest), and Play-Store-ready build are part of the scope. I am open to Google ML Kit, AWS Rekognition, Azure Face API, or a custom TensorFlow Lite model—choose whichever gives the fastest, most reliable response while keeping running costs reasonable.
Key deliverables
• Interactive Figma mock-ups for every screen and state
• Complete Android Studio project (Kotlin preferred) with modular, well-documented code
• Cloud backend with authentication, image storage, and AI inference pipeline
• Unit/UI tests plus a short setup guide so any developer can build from source
• Signed, release-ready APK and Play Console assets
Acceptance criteria
• Gallery-only upload reliably supports current Android photo pickers
• Average analysis turnaround <3 s on 4G/LTE
• Correct face detection on at least 90 % of test photos and basic emotion labelling (happy, sad, neutral, angry, surprised)
• No photo leaves the encrypted store or logs unmasked personal data
If this matches your expertise in mobile AI applications, let’s discuss the best stack and timeline to get the first Android version shipped quickly and cleanly.
Here is the flow I need implemented end-to-end. A user opens the app, chooses a photo stored on the device, the image is uploaded securely to the cloud, an AI model analyses faces, detects their emotions, and returns the findings in seconds. Results should appear in an elegant card-based UI that feels at home on recent Material guidelines.
All design work, backend APIs, AI model integration, security (encryption in transit and at rest), and Play-Store-ready build are part of the scope. I am open to Google ML Kit, AWS Rekognition, Azure Face API, or a custom TensorFlow Lite model—choose whichever gives the fastest, most reliable response while keeping running costs reasonable.
Key deliverables
• Interactive Figma mock-ups for every screen and state
• Complete Android Studio project (Kotlin preferred) with modular, well-documented code
• Cloud backend with authentication, image storage, and AI inference pipeline
• Unit/UI tests plus a short setup guide so any developer can build from source
• Signed, release-ready APK and Play Console assets
Acceptance criteria
• Gallery-only upload reliably supports current Android photo pickers
• Average analysis turnaround <3 s on 4G/LTE
• Correct face detection on at least 90 % of test photos and basic emotion labelling (happy, sad, neutral, angry, surprised)
• No photo leaves the encrypted store or logs unmasked personal data
If this matches your expertise in mobile AI applications, let’s discuss the best stack and timeline to get the first Android version shipped quickly and cleanly.