README.md

metadata

title: MoodLens
emoji: 🔍
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 4.19.2
app_file: app.py
pinned: false
license: mit

🔍 MoodLens - Advanced AI for Text & Emotion Analysis

🌟 Overview

MoodLens is a state-of-the-art multi-task deep learning model that analyzes text to understand human emotions, life events, and linguistic nuances. Built with advanced transformer architecture, it provides comprehensive insights into the emotional and contextual layers of any text.

✨ Features

🎯 Core Capabilities

• Multi-Event Detection: Identifies 50+ different life events (promotions, relationships, health, career changes)
• Emotion Recognition: Detects 12+ emotional states with high accuracy
• Sentiment Analysis: Continuous sentiment scoring from negative (0) to positive (1)
• Sarcasm Detection: Advanced detection of sarcastic and ironic statements
• Temporal Analysis: Understands past, present, and future references
• Certainty Assessment: Measures the confidence level in statements

📊 Technical Highlights

• Model: Microsoft DeBERTa-v3-base (400M parameters)
• Architecture: Multi-task learning with 7 simultaneous outputs
• Accuracy: 85%+ on primary event classification
• Inference Time: <1 second per text
• Max Input: 256 tokens

🚀 Try It Out

Simply enter any text in the input box and click "Analyze Text" to get comprehensive insights about:

• The primary life event being described
• Emotional state and sentiment
• Whether sarcasm is present
• Time references and certainty levels

📝 Example Inputs

Try these examples to see MoodLens in action:

1. Career Success: "I just got promoted to senior manager! All the hard work finally paid off."
2. Life Changes: "Starting my own business next month. Nervous but excited!"
3. Sarcasm Detection: "Oh great, another reorganization. Just what we needed..."
4. Emotional Events: "We're getting married next June! Can't wait to start this new chapter."

🏗️ Architecture

MoodLens uses a sophisticated multi-task learning approach:

Input Text → DeBERTa-v3 → Multi-Head Attention → Task-Specific Heads → 7 Outputs
                ↓
        Shared Feature Extraction
                ↓
    ┌───────────┴───────────┐
    │                       │
Event Classification    Emotion Analysis
Sentiment Scoring      Sarcasm Detection
    └───────────┬───────────┘
                ↓
        Unified Analysis

📱 Mobile & Desktop Optimized

MoodLens features a fully responsive design that works seamlessly on:

• 📱 Mobile phones (iOS & Android)
• 💻 Tablets and laptops
• 🖥️ Desktop computers

🛠️ Technical Stack

• Framework: PyTorch 2.0+
• Base Model: Microsoft DeBERTa-v3-base
• UI Framework: Gradio 4.19
• Deployment: Hugging Face Spaces
• Processing: CPU-optimized for accessibility

📈 Performance Metrics

Task	Accuracy/Score
Event Classification	85.3%
Emotion Detection	82.7%
Sarcasm Detection	89.1%
Sentiment MSE	0.042
Event Group Classification	88.5%

🤝 Use Cases

• Mental Health: Analyze journal entries or social media posts
• Customer Service: Understand customer emotions and concerns
• HR & Recruitment: Analyze employee feedback and reviews
• Content Creation: Ensure appropriate emotional tone
• Personal Development: Track emotional patterns over time

👨‍💻 Creator

Kishan Prajapati

• 🔗 GitHub
• 💼 LinkedIn
• 📧 Contact: kishanprajapati@email.com

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Microsoft Research for DeBERTa-v3
• Hugging Face for the amazing platform
• The open-source ML community

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Made with ❤️ by Kishan Prajapati

If you find MoodLens helpful, please consider giving it a ⭐!