Update README.md

README.md

@@ -7,92 +7,137 @@ tags:
 - mental_health
 ---
 
+---
 ---
 
-# Facial Expression and Mental Health Counseling AI Model
+# BITShyd: Facial Expression and Mental Health Counseling AI
 
 [![Hugging Face](https://img.shields.io/badge/Model-Hugging%20Face-blue)](https://huggingface.co/LOHAMEIT/BITShyd)
 
-### Project Overview
+### 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. 
 
-This AI model combines **facial expression recognition** with **mental health counseling-focused dialogue generation**. Fine-tuned on the `Amod/mental_health_counseling_conversations` dataset using **LoRA** (Low-Rank Adaptation) and **Unsloth**, this model is designed to offer empathetic responses based on visual and conversational cues, suitable for virtual counselors or mental health assistants.
+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.
 
-Key capabilities:
-- **Real-time Emotion Recognition** from facial expressions
-- **Contextually Relevant Responses** in a supportive, conversational tone
+---
 
-### Model Summary
+## Model Details
 
-- **Model Type**: Conversational AI with facial expression support
-- **Training Dataset**: [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations)
-- **Fine-Tuning Techniques**: LoRA and Unsloth for efficient, optimized adaptation
-- **Usage Applications**: Mental health support, virtual assistants, interactive emotional AI
+- **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
 
 ---
 
-## Quick Start
+## Key Features
+
+- **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.
+
+---
+
+## 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
+   ```
 
-1. **Load the Model**
+2. **Load the Model and Tokenizer**
    ```python
-   from transformers import AutoModelForCausalLM, AutoTokenizer
+   from transformers import AutoTokenizer, AutoModelForCausalLM
 
    tokenizer = AutoTokenizer.from_pretrained("LOHAMEIT/BITShyd")
    model = AutoModelForCausalLM.from_pretrained("LOHAMEIT/BITShyd")
    ```
 
-2. **Prepare the Input**
-   - Ensure the input text or image follows the required pre-processing steps for facial expression recognition.
-   - Use `transformers` for text and facial expression embeddings to create a blended emotional context.
+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.
 
-3. **Generate a Response**
+4. **Generate a Response**
    ```python
-   inputs = tokenizer("User input text here", return_tensors="pt")
-   output = model.generate(**inputs)
+   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))
    ```
 
 ---
 
-### Training and Fine-Tuning
+## Usage Examples
+
+### 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))
+```
+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."
 
-This model was fine-tuned with **LoRA** and **Unsloth**:
+---
 
-- **LoRA** enables efficient training with limited resources by reducing the dimensionality of model parameters, while retaining high accuracy.
-- **Unsloth** minimizes latency and optimizes response generation, improving the model's suitability for real-time applications.
+## Model Training and Fine-Tuning Details
 
-1. **Install LoRA & Unsloth**:
-   ```bash
-   pip install lora unsloth
-   ```
+This model was trained with **LoRA** and **Unsloth**:
 
-2. **Fine-Tune on Custom Dataset** (if desired):
-   ```python
-   from lora import LoraTrainer
-   trainer = LoraTrainer(model, dataset="Amod/mental_health_counseling_conversations")
-   trainer.train()
-   ```
+- **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.
 
-### Model Details
+Training Configurations:
 
-| Parameter       | Description                        |
-|-----------------|------------------------------------|
-| Model Size      | 8 Billion Parameters              |
-| Fine-Tuning     | LoRA + Unsloth                    |
+| Parameter       | Description                         |
+|-----------------|-------------------------------------|
+| Model Size      | 8 Billion Parameters               |
+| Epochs          | 3                                  |
+| Learning Rate   | 5e-5                               |
+| Batch Size      | 8                                  |
 | Dataset         | Amod/mental_health_counseling_conversations |
-| Primary Use     | Mental Health AI, Virtual Support |
+| Optimizations   | LoRA and Unsloth                   |
 
-### Example Use Case
+---
+
+## Future Work
 
-The model is designed to recognize and interpret facial expressions alongside counseling conversations. This interaction facilitates emotionally supportive responses, tailored for user needs in mental health applications or personal emotional assistants.
+- **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.
 
 ---
 
 ## License
 
-This model and dataset are licensed for non-commercial use. For more details, see [LICENSE](LICENSE.md).
+This model is available under the Apache 2.0 License. For detailed terms, refer to the [LICENSE](LICENSE.md) file in the repository.
 
 ---
 
-Explore the model on Hugging Face: [LOHAMEIT/BITShyd](https://huggingface.co/LOHAMEIT/BITShyd)
+### 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!
+
+--- 
 
----