EmoSense — Multi-Emotion Recognition System
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Built by Youssef Salem Hassan
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Overview
Graduation project for AI-based customer sentiment analysis across text, voice, and video
My role
Team lead: built the entire Flutter app in one week (Clean Architecture/BLoC/20+ flows) alongside the multimodal ML pipeline.
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The Problem
Organizations struggle to operationalize emotion signals from text, voice, and video at enterprise scale while keeping latency acceptable and access tightly governed.
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The Solution
I built EmoSense in Flutter with Clean Architecture, BLoC, repository patterns, and structured navigation—pairing the client with TensorFlow and Python pipelines for multimodal inference, RBAC‑aware portals, and analytics dashboards tuned for near real‑time monitoring.
Technologies used
17Languages
Dart 3.0+Python
Platforms
Flutter 3.32.1Material Design 3
Architecture & state
Clean ArchitectureBLoC/CubitRepository PatternState Management
Backend, data & cloud
FlaskAudio ProcessingREST API
Other
TensorFlowComputer VisionMachine LearningGetIt DIAnimation SystemEnterprise Security
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Key Outcomes
- Multimodal analysis roadmap spanning text, voice, and video with production-minded UX scaffolding.
- Role‑based admin and employee experiences with analytics oriented toward CX optimization.
- Graduate research project demonstrating CV + ML depth aligned with Nile University work.
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Screenshots & gallery
Onboarding & Authentication
9 imagesEmployee Home & Tools
2 imagesVideo Analysis
4 imagesVoice Analysis
3 imagesText Analysis
2 imagesSupport Tickets
3 imagesEmployee Profile
3 imagesAdmin Panel
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