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  1. README.md +28 -5
README.md CHANGED
@@ -8,13 +8,36 @@ app_port: 8501
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  tags:
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  - streamlit
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  pinned: false
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- short_description: Streamlit template space
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  license: apache-2.0
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  ---
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- # Welcome to Streamlit!
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- Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
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- If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
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- forums](https://discuss.streamlit.io).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  tags:
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  - streamlit
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  pinned: false
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+ short_description: Streamlit Demo App
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  license: apache-2.0
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  ---
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+ # 📧 Spam Classifier Agent Live Demo
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+ A Streamlit web application that leverages a fine-tuned GPT-2 model to classify text messages as either "SPAM" or "NOT SPAM". This agent helps users quickly determine the nature of a message. Try it here @ [SoggyBurritos/Spam_Classifier_Agent](https://huggingface.co/spaces/SoggyBurritos/Spam_Classifier_Agent)
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+ ## Features
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+
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+ * **Text Input:** Easily enter any text message for classification.
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+ * **Real-time Prediction:** Get instant results on whether the message is spam or not.
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+ * **Probability Scores:** View the confidence scores for both "SPAM" and "NOT SPAM" categories.
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+ * **Intuitive UI:** A clean and user-friendly interface built with Streamlit.
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+ * **Fine-tuned GPT-2 Model:** Utilizes a powerful transformer model for accurate classification.
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+
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+ ## 💻 How to Use
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+
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+ 1. **Enter Text:** Type or paste the message you want to classify into the provided text area.
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+ 2. **Analyze:** Click the "Analyze Text" button.
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+ 3. **View Results:** The application will display the prediction (SPAM or NOT SPAM) along with the probability scores.
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+
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+ ## ⚙️ Technical Details for Hugging Face Spaces
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+
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+ **App File Location:**
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+ The main Streamlit application script is located at `streamlit/app/main.py`. Hugging Face Spaces will be configured to run this specific file.
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+
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+ **Dependencies:**
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+ All required Python packages for this deployment is listed in `requirements.txt`.
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+
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+ **Model & Data:**
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+ * The fine-tuned GPT-2 model weights (`Spam-Classifier-GPT2-Model.pt`) are expected to be in the `models/` directory at the root of the repository.
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+ * The application also relies on data preparation scripts which may download or create necessary data files.