README v1

README.md

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 title: PhishGuardian AI
-emoji: 💬
+emoji: 🔥
 colorFrom: yellow
 colorTo: purple
 sdk: gradio
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 license: apache-2.0
 short_description: UDSM AI-powered tool for real-time phishing email detection.
 ---
+Phishing Email Detection Space
+Welcome to the Phishing Email Detection Hugging Face Space! This project provides an interactive web interface to classify emails as legitimate or phishing using a fine-tuned DistilBERT model (cybersectony/phishing-email-detection-distilbert_v2.4.1). Built with Gradio, this Space allows users to input email text and receive predictions with confidence scores and probability distributions.
+Table of Contents
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+Overview
+Features
+How It Works
+Usage
+Installation (For Local Development)
+Model Details
+Contributing
+License
+Contact
+
+Overview
+This Space deploys a DistilBERT-based model to detect phishing emails by classifying input text into one of four categories: Legitimate Email, Phishing URL, Legitimate URL, or Phishing URL (Alt). The model is hosted on Hugging Face and integrated with a Gradio interface for easy interaction. Users can input email text and instantly view the predicted classification along with confidence scores.
+Features
+
+Interactive Interface: Input email text via a user-friendly Gradio web interface.
+Real-Time Predictions: Get immediate classification results with confidence scores.
+Detailed Output: View probabilities for all classes (Legitimate Email, Phishing URL, Legitimate URL, Phishing URL Alt).
+Lightweight Model: Uses DistilBERT for efficient inference, suitable for CPU-based environments.
+Open Source: Code and model are accessible for further customization.
+
+How It Works
+
+The user inputs email text into the Gradio interface.
+The text is tokenized using the DistilBERT tokenizer.
+The fine-tuned DistilBERT model processes the input and outputs probabilities for each class.
+The interface displays the most likely classification, confidence score, and all class probabilities.
+
+Usage
+
+Access the Space: Visit the Hugging Face Space URL (e.g., https://<your-username>-<space-name>.hf.space).
+Enter Email Text: Type or paste the email content into the provided text box.
+Get Prediction: Click the "Submit" button to view the classification results.
+Interpret Results: The output includes:
+Prediction: The most likely class (e.g., "Phishing URL").
+Confidence: The probability score for the predicted class.
+All Probabilities: Probability scores for all four classes.
+
+
+
+Example Input:
+Subject: Urgent: Verify Your Account Now
+Dear Customer, your account has been flagged. Click here to verify: [suspicious-link.com].
+
+Example Output:
+Prediction: Phishing URL
+Confidence: 0.9278
+All Probabilities:
+- Legitimate Email: 0.0123
+- Phishing URL: 0.9278
+- Legitimate URL: 0.0345
+- Phishing URL (Alt): 0.0254
+
+Installation (For Local Development)
+If you want to run this project locally or contribute to its development, follow these steps:
+
+Clone the Repository:
+git clone https://huggingface.co/spaces/<your-username>/<your-space-name>
+cd <your-space-name>
+
+
+Install Dependencies:Create a virtual environment and install the required packages:
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+pip install -r requirements.txt
+
+
+Run the Application:Launch the Gradio interface locally:
+python app.py
+
+
+Access Locally:Open the provided local URL (e.g., http://127.0.0.1:7860) in your browser.
+
+
+Requirements (listed in requirements.txt):
+transformers
+torch
+gradio
+
+Model Details
+
+Model: cybersectony/phishing-email-detection-distilbert_v2.4.1
+Architecture: DistilBERT (fine-tuned for sequence classification)
+Classes:
+Legitimate Email
+Phishing URL
+Legitimate URL
+Phishing URL (Alt)
+
+
+Input: Text (max length: 512 tokens)
+Output: Probabilities for each class, with the highest probability determining the predicted class.
+
+The model is hosted on Hugging Face and loaded directly in the Space. For private models, ensure you set the HF_TOKEN environment variable in your Space settings.
+Contributing
+Contributions are welcome! To contribute:
+
+Fork the repository on Hugging Face.
+Create a new branch for your changes (git checkout -b feature/your-feature).
+Commit your changes (git commit -m "Add your feature").
+Push to your fork (git push origin feature/your-feature).
+Open a pull request on the Space’s repository.
+
+Please ensure your code follows the project’s style and includes tests where applicable.
+
+License
+This project is licensed under the APACHE 2.0. See the LICENSE file for details.
+
+Contact
+For questions or feedback, please reach out via:
+
+Hugging Face: [https://huggingface.co/MUFASA25]
+Email: [[email protected]]
+Issues: Open an issue on the Space’s repository.
+
+Thank you for using the Phishing Email Detection Space!