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# EmoSense
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
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[](https://github.com/haybnzz/EmoSense)
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[](https://github.com/haybnzz/EmoSense/blob/main/LICENSE)
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[](https://www.python.org/downloads/)
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[](https://github.com/haybnzz/EmoSense/issues)
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[](https://github.com/haybnzz/EmoSense/pulls)
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[](https://github.com/haybnzz/EmoSense/stargazers)
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
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[](https://huggingface.co/datasets/haydenbanz/EmoSense_Datsets)
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[](https://huggingface.co/haydenbanz/EmoSense_model)
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[](https://huggingface.co/spaces/haydenbanz/EmoSense)
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> EmoSense: An advanced AI model designed to detect emotions from facial images and infer psychological behavior. Trained on a diverse dataset, EmoSense identifies seven core emotions—angry, disgusted, fearful, happy, neutral, sad, and surprised—with high accuracy. Using deep learning, it analyzes visual cues to provide insights into emotional states and potential behavioral tendencies, making it a powerful tool for understanding human affect in real-time.
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## Purpose of EmoSense
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- Harness the power of machine learning to understand and interpret human emotions from image data.
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- Leverage advanced natural language processing (NLP) techniques for sentiment analysis and emotion detection.
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- Predict emotional trends to provide insights into written content.
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- Offer an accessible tool for personal use, research, or non-commercial applications.
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- Explore emotional undertones with integration of Hugging Face models and datasets.
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- Encourage collaboration and innovation under a non-commercial CC BY-NC 4.0 license.
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# Installation Guide for EmoSense
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# Installation Guide for EcomPredict
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## Step 1: Fork the Repository
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1. Go to the [EcomPredict GitHub repository](https://github.com/haybnzz/EmoSense).
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2. Click on the "Fork" button in the upper right corner of the page to create a copy of the repository under your own GitHub account.
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`git clone https://github.com/haybnzz/EmoSense.git`
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`cd EmoSense`
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`pip install -r requirements.txt`
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`python app.py`
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`python app_ui.py`
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`python app_cmd.py`
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## EmoSense Resources
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- **Datasets:** [EmoSense Datasets](https://huggingface.co/datasets/haydenbanz/EmoSense_Datsets)
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- **Models:** [EmoSense Models](https://huggingface.co/haydenbanz/EmoSense_model)
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# EmoSense Project Documentation
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## Data Reading
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- **Command:** `streamlit run app_ui.py`
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- **Description:** Launches a Streamlit GUI interface where you can upload text files or datasets for emotion analysis.
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## Data Conversion to Model
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- **Command:** `python app.py`
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- **Description:** Converts text datasets into a trained AI model for emotion detection and sentiment analysis.
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## Heatmap Creation
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- **Command:** `python app_cmd.py`
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- **Description:** Generates a heatmap of emotional intensity from text data. Modify the file path in `app_cmd.py` to specify the input dataset or text file to analyze.
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## 📜 License
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This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. See the [LICENSE](LICENSE) file for more details.
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**Unauthorized use is strictly prohibited.**
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📧 Email: [email protected]
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### Contributors and Developers
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[<img src="https://avatars.githubusercontent.com/u/67865621?s=64&v=4" width="64" height="64" alt="haybnzz">](https://github.com/haybnzz)
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[<img src="https://avatars.githubusercontent.com/u/144106684?s=64&v=4" width="64" height="64" alt="Glitchesminds">](https://github.com/Glitchesminds)
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## ☕ Support
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Donate via Monero: `45PU6txuLxtFFcVP95qT2xXdg7eZzPsqFfbtZp5HTjLbPquDAugBKNSh1bJ76qmAWNGMBCKk4R1UCYqXxYwYfP2wTggZNhq`
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[<img src="https://img.shields.io/badge/Copy-Address-FFD166?style=flat&logo=clipboard&logoColor=white" alt="Copy Monero Address" onclick="navigator.clipboard.writeText('45PU6txuLxtFFcVP95qT2xXdg7eZzPsqFfbtZp5HTjLbPquDAugBKNSh1bJ76qmAWNGMBCKk4R1UCYqXxYwYfP2wTggZNhq')">](javascript:void(0))
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