README.md

---
license: mit
language:
- en
metrics:
- accuracy
base_model:
- microsoft/resnet-50
new_version: google/vit-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- pytorch
- emotion-detection
- facial-expressio
- image-classification
- deep-learning
- cnn
---
# 🎭 Face Expression Detector

A deep learning model that classifies facial expressions in grayscale images into one of seven core emotions. Designed for applications in **emotion analytics**, **human-computer interaction**, and **psychological research**.

---

## 📊 Model Overview

This model takes **48x48 grayscale face images** and classifies them into:

- 😠 Angry  
- 🤢 Disgust  
- 😨 Fear  
- 😄 Happy  
- 😢 Sad  
- 😲 Surprise  
- 😐 Neutral

**Dataset**: [FER2013](https://www.kaggle.com/datasets/msambare/fer2013)  
**Training Samples**: 28,709  
**Testing Samples**: 3,589

---

## 🧠 Model Architecture

- 📦 **Custom CNN**
  - 3 Convolutional Layers
  - Batch Normalization
  - ReLU Activation
  - Dropout for regularization
- 📈 Optimizer: `Adam`
- 🔥 Loss Function: `Categorical Crossentropy`
- ⏱️ Epochs: `100`

---

## ✅ Performance

> 📌 *Add your actual performance metrics here:*

- Accuracy on FER2013 Test Set: **~1.0%**

---

## 🗂️ Required Files

- `model.h5` → Model Weights  
- `config.json` → Configuration file *(Transformers-based)*  
- `preprocessor_config.json` → Preprocessing setup *(if needed)*  
- `requirements.txt` → Python dependencies

---

## 🚀 Use Cases

- 🎮 Real-time emotion feedback in games or virtual assistants  
- 🎓 Emotion analysis for psychological and behavioral studies  
- 🎥 Enhancing video-based UX with dynamic emotion tracking

---

## ⚠️ Limitations

- Works best with **centered 48x48 grayscale faces**
- **Face detection (e.g., MTCNN)** required before prediction
- FER2013's demographic diversity is limited → potential bias

---

## ⚙️ Installation

Follow these steps to set up the environment and dependencies:
--pip install -r requirements.txt
torch>=1.9.0
transformers>=4.20.0
pillow>=8.0.0
### 1. Clone the Repository

git clone https://github.com/TRavi8688/Mood-Based-Music-Player
cd mood_detector

##🧪 How to Use (Transformers-based)

Follow these steps to preprocess an image and predict facial expression using the pre-trained Transformers-based model:
Python
```bash
from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import torch
```
### 1. Load Model and Preprocessor
```bash
# STEP 1: Install dependencies
!pip install tensorflow pillow numpy

# STEP 2: Download model file using `requests`
import requests

model_url = "https://huggingface.co/ravi86/mood_detector/resolve/main/my_model.h5"
model_path = "my_model.h5"

# Download the file
response = requests.get(model_url)
with open(model_path, "wb") as f:
    f.write(response.content)

print("✅ Model downloaded successfully!")
```
 2. Load and Preprocess the Image
```bash
image_path = "your_image.jpg"  # 🔁 Replace with your image file
image = Image.open(image_path).convert("L").resize((48, 48))  # Convert to grayscale and resize
```
# 3. Make Predictions
```bash
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=-1)  # Convert logits to probabilities
predicted_class = probs.argmax().item()  # Get the predicted class index
```
# 4. Interpret the Result
```bash
emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
print(f"Predicted Emotion: {emotions[predicted_class]}")
```
☁️ Deploy to Hugging Face Hub
```bash
Use these commands to prepare and push your model to the Hugging Face Hub:
Bash

# Step 1: Install & Login
pip install huggingface_hub
huggingface-cli login

from huggingface_hub import upload_folder

upload_folder(
    folder_path="path/to/mood_detector",
    repo_id="ravi86/mood_detector",
    repo_type="model",
    commit_message="🚀 Upload mood detection model"
)
```
###
🧭 Ethical Considerations

    ⚖️ Bias: The FER2013 dataset may exhibit biases in demographic representation. Exercise caution when interpreting results across diverse populations.
    🔒 Privacy: Ensure strict compliance with data privacy laws (e.g., GDPR, CCPA) when using this model on personal or sensitive images. Do not use without explicit consent.
    ❗ Misuse: This model is not intended for unauthorized surveillance, profiling, or any other unethical applications.
    
###
👤 Contact
  📬 For questions, support, or collaborations:
  Hugging Face → @ravi86
  Gmail→ travikumar6789@gmail.com
 ⭐ If you find this project useful, consider giving a star or contributing!