Update README.md

README.md

@@ -6,137 +6,108 @@ base_model:
 tags:
 - mental_health
 ---
-
----
-
-# BITShyd: Facial Expression and Mental Health Counseling AI
+# BITShyd: AI-Enhanced Facial Expression & Mental Health Counseling
 
 [![Hugging Face](https://img.shields.io/badge/Model-Hugging%20Face-blue)](https://huggingface.co/LOHAMEIT/BITShyd)
 
 ### Project Summary
 
-**BITShyd** is an advanced AI model that combines **facial expression recognition** with **mental health counseling dialogues**, designed to offer empathetic responses based on both visual and conversational cues. This project fine-tunes a conversational AI with the **Amod/mental_health_counseling_conversations** dataset, adapting it specifically for virtual counseling and emotional support applications. 
-
-The model leverages LoRA (Low-Rank Adaptation) and Unsloth fine-tuning techniques for efficient adaptation, making it suitable for use on various hardware setups, from personal devices to cloud-based applications.
-
----
-
-## Model Details
-
-- **Model Type**: Conversational AI with emotional intelligence features
-- **Dataset**: [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations)
-- **Fine-Tuning Methods**: LoRA & Unsloth for optimized performance and low latency
-- **Primary Applications**: Virtual mental health support, empathetic AI assistants, interactive emotional response models
+**BITShyd** is an innovative AI project developed at BITS Pilani, Hyderabad, designed to bridge the gap between **facial expression recognition** and **mental health counseling**. Using a combination of conversational AI fine-tuned on the **Amod/mental_health_counseling_conversations** dataset and real-time facial expression analysis, the model provides contextual responses tailored to the emotional state of the user. This empathetic AI supports mental health applications and interactive emotional support systems. 
 
 ---
 
-## Key Features
+## Key Features and Technology
 
-- **Real-Time Facial Expression Recognition**: Capable of identifying emotional expressions such as happiness, sadness, anger, surprise, and neutrality.
-- **Empathetic, Contextually Aware Responses**: Trained specifically for counseling-based responses, this model interacts in an emotionally supportive way.
-- **Scalable Fine-Tuning Techniques**: LoRA and Unsloth allow for efficient, resource-light tuning, making the model adaptable to different devices.
+- **Facial Expression Recognition**: Detects emotions (happy, sad, angry, surprised, etc.) using real-time video input, enhanced with `face-api.js`.
+- **Conversational AI for Mental Health**: The model generates empathetic, supportive responses in mental health contexts, using fine-tuning on the Amod dataset.
+- **Low-Rank Adaptation (LoRA)** and **Unsloth Fine-Tuning**: These techniques streamline adaptation and latency, allowing use across hardware setups, from personal devices to cloud platforms.
 
 ---
 
-## Quickstart Guide
-
-Here’s how to get started with using this model in your own applications.
-
-### Installation and Setup
-
-1. **Install Hugging Face Transformers and Required Libraries**:
-   ```bash
-   pip install transformers torch
-   ```
-
-2. **Load the Model and Tokenizer**
-   ```python
-   from transformers import AutoTokenizer, AutoModelForCausalLM
-
-   tokenizer = AutoTokenizer.from_pretrained("LOHAMEIT/BITShyd")
-   model = AutoModelForCausalLM.from_pretrained("LOHAMEIT/BITShyd")
-   ```
+## Technical Overview
 
-3. **Preparing Input**
-   - **Text Input**: This model uses text prompts, ideally incorporating facial expression indicators for contextual awareness.
-   - **Image Input** (Optional): For real-time interaction, integrate with a facial expression API to enhance response generation based on user expressions.
+- **Base Model**: LLaMA 3.1 (8B parameters) on Hugging Face
+- **Dataset**: [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations)
+- **Optimization**: LoRA for lightweight tuning and Unsloth for improved inference latency
+- **Supported Use Cases**: Virtual mental health counseling, empathetic AI interactions, real-time emotional analysis
 
-4. **Generate a Response**
-   ```python
-   input_text = "Hello, I feel anxious today."
-   inputs = tokenizer(input_text, return_tensors="pt")
-   output = model.generate(**inputs, max_length=50)
-   print(tokenizer.decode(output[0], skip_special_tokens=True))
-   ```
+### Fine-Tuning Details
 
----
+The model utilizes **LoRA** and **Unsloth** fine-tuning, which make it adaptable to a range of devices by reducing the resource load without compromising on performance:
 
-## Usage Examples
+- **Parameter-Efficient Training**: LoRA keeps the model’s core knowledge while adapting to new data, critical for sensitive applications like mental health.
+- **Latency Optimization**: Unsloth helps reduce response time, essential for real-time interaction.
+- **Training Configurations**: 
 
-### 1. Mental Health Support Assistant
-```python
-input_text = "I'm feeling overwhelmed and don't know how to manage my stress."
-inputs = tokenizer(input_text, return_tensors="pt")
-output = model.generate(**inputs, max_length=50)
-print(tokenizer.decode(output[0], skip_special_tokens=True))
-```
-Expected Response:
-> "I'm here for you. It sounds like things are challenging right now. Let's take a deep breath together. Would you like to talk more about what's overwhelming you?"
-
-### 2. Emotionally Responsive AI Assistant
-This example integrates with a facial expression API to adjust responses based on detected emotions (like sadness or happiness).
-
-```python
-detected_emotion = "sadness"  # Detected through facial expression analysis
-input_text = "I've been feeling lonely lately."
-inputs = tokenizer(f"{detected_emotion} | {input_text}", return_tensors="pt")
-output = model.generate(**inputs, max_length=50)
-print(tokenizer.decode(output[0], skip_special_tokens=True))
+| Parameter       | Value                             |
+|-----------------|-----------------------------------|
+| Model Size      | 8 Billion Parameters              |
+| Epochs          | 3                                 |
+| Learning Rate   | 5e-5                              |
+| Batch Size      | 8                                 |
+| Dataset         | Amod/mental_health_counseling_conversations |
+| Optimizations   | LoRA and Unsloth                  |
+
+### Code for Facial Expression Recognition 
+
+Below is a snippet using `face-api.js` for real-time expression detection and emoji overlay based on emotions detected:
+
+```javascript
+const video = document.getElementById('video');
+const expressionsToEmoji = { happy: '😊', sad: '😢', angry: '😠', surprised: '😮', fearful: '😨' };
+
+async function loadModels() {
+    await faceapi.nets.tinyFaceDetector.loadFromUri('models');
+    await faceapi.nets.faceExpressionNet.loadFromUri('models');
+    await startVideo();
+}
+
+async function startVideo() {
+    const stream = await navigator.mediaDevices.getUserMedia({ video: true });
+    video.srcObject = stream;
+}
+
+async function detectFaces() {
+    const detections = await faceapi.detectAllFaces(video, new faceapi.TinyFaceDetectorOptions())
+        .withFaceExpressions();
+    
+    // Render emoji based on emotion
+    detections.forEach(detection => {
+        const expressions = detection.expressions;
+        const topExpression = Object.keys(expressions).reduce((a, b) => expressions[a] > expressions[b] ? a : b);
+        const emoji = expressionsToEmoji[topExpression] || '🙂';
+        // Code to overlay emoji on video feed...
+    });
+}
+
+loadModels();
 ```
-Expected Response:
-> "I'm sorry you're feeling this way. Loneliness can be really tough. Sometimes sharing your feelings can help. I'm here to listen if you'd like to talk."
 
 ---
 
-## Model Training and Fine-Tuning Details
+## Pros & Cons of Fine-Tuning Techniques
 
-This model was trained with **LoRA** and **Unsloth**:
+### Advantages
+- **Efficient Adaptability**: LoRA enables fine-tuning with minimal additional parameters, making the model flexible for different datasets without extensive resources.
+- **Reduced Latency**: Unsloth optimizes real-time response speed, vital for real-time interactions in mental health applications.
+- **Scalability**: These techniques allow the model to run on a range of devices, expanding access to diverse user bases.
 
-- **LoRA (Low-Rank Adaptation)**: LoRA enables the model to retain core knowledge while adapting efficiently to new data, making it ideal for nuanced tasks like mental health counseling.
-- **Unsloth**: Unsloth enhances inference speed, allowing the model to process requests with lower latency, suitable for real-time interaction scenarios.
-
-Training Configurations:
-
-| Parameter       | Description                         |
-|-----------------|-------------------------------------|
-| Model Size      | 8 Billion Parameters               |
-| Epochs          | 3                                  |
-| Learning Rate   | 5e-5                               |
-| Batch Size      | 8                                  |
-| Dataset         | Amod/mental_health_counseling_conversations |
-| Optimizations   | LoRA and Unsloth                   |
+### Disadvantages
+- **Limited Transfer to Drastically Different Domains**: LoRA and Unsloth are efficient within the trained domain but may struggle with applications vastly different from the original training data.
+- **Computational Constraints**: Although reduced, fine-tuning large models can still demand significant resources for high-quality performance in real-world applications.
 
 ---
 
-## Future Work
+## Why Building Projects is Crucial Beyond DSA Skills
 
-- **Advanced Emotion Detection**: Plan to integrate a broader range of emotions and body language cues.
-- **Interactive Widgets**: Adding Hugging Face widget for real-time interactions directly on this model’s page.
-- **Deployment Options**: Explore integration with cloud-based platforms for widespread access.
+Building real-world projects like BITShyd is invaluable compared to simply mastering algorithms and data structures (DSA). While DSA provides foundational skills for coding efficiency, **project-based learning** fosters critical skills like problem-solving, innovation, and practical application. These skills are essential in building technology that interacts with the real world, like empathetic AI. In a 9-to-5 job, growth is often linear, but personal projects allow you to create, innovate, and potentially develop tech that impacts millions—especially in fields like AI, where emotional intelligence and empathy are groundbreaking.
 
----
-
-## License
-
-This model is available under the Apache 2.0 License. For detailed terms, refer to the [LICENSE](LICENSE.md) file in the repository.
+BITShyd represents this mindset, and we encourage developers to focus on projects that break conventional boundaries and offer more than predefined answers to problems. At BITS Pilani, we aim to encourage building, experimenting, and understanding the end-to-end process of creating impactful technology.
 
 ---
 
-### Explore the Model
-
-Interact with the model here: [LOHAMEIT/BITShyd](https://huggingface.co/LOHAMEIT/BITShyd) 
-
-For any feedback or collaboration requests, feel free to reach out on Hugging Face or GitHub!
-
---- 
+## Future Enhancements
 
+- **Expanded Emotion Detection**: Plan to integrate a broader set of emotional expressions and even body language for deeper analysis.
+- **Deployment Options**: Considering integration with cloud platforms for broad accessibility and robust interaction.
+- **Error Handling**: Addressing the recent challenge with the Ollama API, ensuring smooth integration for stable performance.