Update README.md

README.md

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-# Face Expression Detector
-
-## Model Overview
-This deep learning model classifies facial expressions in 48x48 pixel grayscale images into one of seven emotion categories: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. Trained on the FER2013 dataset with **28,709 training images** and evaluated on **3,589 test images**, the model is designed for applications such as emotion analysis, human-computer interaction, and psychological research. It processes centered face images, making it suitable for real-world emotion detection scenarios.
-
-## Model Details
-- **Architecture**: [Specify the model architecture, e.g., "Custom CNN with multiple convolutional and dense layers." Update this with your model details.]
-- **Training Data**: The model was trained on the FER2013 dataset, consisting of:
-  - **Training Set**: 28,709 grayscale images (48x48 pixels) of faces, automatically registered to be centered and uniformly sized.
-  - **Public Test Set**: 3,589 grayscale images (48x48 pixels).
-  - The dataset includes diverse facial expressions labeled into seven categories.
-- **Classes**: The model predicts one of seven emotions:
-  - 0: Angry
-  - 1: Disgust
-  - 2: Fear
-  - 3: Happy
-  - 4: Sad
-  - 5: Surprise
-  - 6: Neutral
-- **Performance**: [Include metrics if available, e.g., "Achieves ~70% accuracy on the FER2013 public test set"]. Performance depends on preprocessing and augmentation techniques used during training.
-- **Training Details**:
-  - Epochs: [Add number of epochs, if known, e.g., 50]
-  - Optimizer: [e.g., Adam, SGD]
-  - Loss Function: [e.g., Categorical Crossentropy]
-  - Hardware: [e.g., Trained on GPU/TPU, if known]
-- **Input**: Grayscale images of size 48x48 pixels with centered faces. Preprocessing (e.g., normalization, face detection) is recommended for optimal performance.
-- **Output**: Probability distribution over the seven emotion classes, with the predicted class corresponding to the highest probability.
-
-## Required Files
-To use this model, ensure the following files are included:
-- **Model Weights**: `model.pt` (PyTorch) or `model.h5` (TensorFlow), containing the trained weights.
-- **Configuration File**: `config.json` (for Transformers-based models) or a custom script defining the architecture (for non-Transformers models).
-- **Preprocessor Configuration**: `preprocessor_config.json` (if using Transformers) or documented preprocessing steps in this README.
-- **Requirements**: `requirements.txt` listing dependencies (e.g., `torch`, `transformers`, `pillow`).
-
-## Intended Use
-This model is suitable for:
-- **Emotion Analysis**: Real-time emotion detection in video analysis or customer feedback systems.
-- **Human-Computer Interaction**: Enhancing user experiences in gaming, virtual assistants, or interactive kiosks.
-- **Psychological Research**: Supporting studies in affective computing or emotional behavior analysis.
-- **Educational Tools**: Assisting in emotional intelligence training or teaching applications.
-
-## Limitations
-- Optimized for 48x48 grayscale images with centered faces; performance may degrade with misaligned faces, poor lighting, or occlusions.
-- The FER2013 dataset may lack sufficient diversity in demographics or cultural expressions, potentially affecting accuracy across varied populations.
-- Requires preprocessed input (e.g., face detection using MTCNN or OpenCV) for raw images.
-
-## How to Use
-1. **Install Dependencies**:
-   ```bash
-   pip install -r requirements.txt
+Face Expression Detector
+Model Overview
+This deep learning model classifies facial expressions in 48x48 pixel grayscale images into one of seven emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. Trained on the FER2013 dataset with 28,709 training images and evaluated on 3,589 test images, it’s designed for applications like emotion analysis, human-computer interaction, and psychological research.
+Model Details
+
+Architecture: [ "Custom CNN with 3 convolutional layers."]
+Training Data:
+Dataset: FER2013
+Training Set: 28,709 grayscale images (48x48 pixels), centered faces.
+Test Set: 3,589 grayscale images (48x48 pixels).
+
+
+Classes:
+0: Angry
+1: Disgust
+2: Fear
+3: Happy
+4: Sad
+5: Surprise
+6: Neutral
+
+
+Performance: [ "Achieves ~1.0% accuracy on the FER2013 test set."]
+Training Details:
+Epochs: [ 50]
+Optimizer: [ Adam]
+Loss Function: [Categorical Crossentropy]
+
+
+Input: Grayscale images (48x48 pixels, centered faces). Preprocessing (e.g., normalization) is recommended.
+Output: Probability distribution over the seven emotions.
+
+Required Files
+
+model.pt (PyTorch) or model.h5 (TensorFlow): Model weights.
+config.json: Model configuration (if Transformers-based).
+preprocessor_config.json: Preprocessing config (if Transformers-based).
+requirements.txt: Dependencies.
+
+Intended Use
+
+Emotion Analysis: Real-time emotion detection in videos or feedback systems.
+Human-Computer Interaction: Enhancing user experiences in gaming or virtual assistants.
+Psychological Research: Supporting studies in affective computing.
+
+Limitations
+
+Optimized for 48x48 grayscale images; may struggle with misaligned faces or poor lighting.
+FER2013 dataset may lack diversity, affecting accuracy across demographics.
+Requires preprocessed input (e.g., face detection with MTCNN).
+
+How to Use
+Install Dependencies
+pip install -r requirements.txt
+
+Example requirements.txt:
+torch>=1.9.0
+transformers>=4.20.0
+pillow>=8.0.0
+
+Load the Model (Transformers-based)
+'''from transformers import AutoModelForImageClassification, AutoImageProcessor
+model = AutoModelForImageClassification.from_pretrained("ravi86/mood_detector")
+processor = AutoImageProcessor.from_pretrained("ravi86/mood_detector")'''
+
+Preprocess and Predict
+from PIL import Image
+import torch
+
+image = Image.open("path_to_image.jpg").convert("L")  # Convert to grayscale
+image = image.resize((48, 48))  # Resize to 48x48
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+predictions = torch.softmax(outputs.logits, dim=-1)
+predicted_class = predictions.argmax().item()
+emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
+print(f"Predicted emotion: {emotions[predicted_class]}")
+
+Uploading to Hugging Face
+Install the Hub
+pip install huggingface_hub
+
+Log In
+huggingface-cli login
+
+Push the Model
+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 model"
+)
+
+Ethical Considerations
+
+Bias: FER2013 may have biases in demographic representation.
+Privacy: Ensure compliance with data privacy laws (e.g., GDPR).
+Misuse: Avoid unauthorized surveillance or profiling.
+
+Contact
+Contact [ravi86] or [travikumar6789@gmial.com] on Hugging Face for inquiries or contributions.