| ## Deep Prediction Hub | |
| Overview | |
| Welcome to Deep Prediction Hub, a Streamlit web application that provides two deep learning-based tasks: Sentiment Classification and Tumor Detection. | |
| Tasks | |
| 1. Sentiment Classification | |
| This task involves classifying the sentiment of a given text into "Positive" or "Negative". Users can input a review, and the application provides the sentiment classification using various models. | |
| 2.Tumor Detection | |
| In Tumor Detection, users can upload an image, and the application uses a Convolutional Neural Network (CNN) model to determine if a tumor is present or not. | |
| Getting Started | |
| Prerequisites | |
| Python 3.6 or higher | |
| Required packages: streamlit, numpy, cv2, PIL, tensorflow | |
| Pre-trained models: PP.pkl, BP.pkl, DP.keras, RN.keras, LS.keras, CN.keras | |
| Trained IMDb word index: Ensure the IMDb word index is available for sentiment classification. | |
| Installation | |
| Clone the repository: git clone https://github.com/yourusername/deep-prediction-hub.git | |
| Usage | |
| Access the application by opening the provided URL after running the Streamlit app. | |
| Choose between "Sentiment Classification" and "Tumor Detection" tasks. | |
| Sentiment Classification | |
| Enter a review in the text area. | |
| Select a model from the dropdown. | |
| Click "Submit" and then "Classify Sentiment." | |
| Tumor Detection | |
| Upload an image using the file uploader. | |
| Click "Detect Tumor" to perform tumor detection. | |
| Models | |
| Perceptron (PP.pkl): Perceptron-based sentiment classification model. | |
| Backpropagation (BP.pkl): Backpropagation-based sentiment classification model. | |
| DNN (DP.keras): Deep Neural Network sentiment classification model. | |
| RNN (RN.keras): Recurrent Neural Network sentiment classification model. | |
| LSTM (LS.keras): Long Short-Term Memory sentiment classification model. | |
| CNN (CN.keras): Convolutional Neural Network tumor detection model. | |
| Contributing | |
| Feel free to contribute by opening issues or submitting pull requests. Please follow the contribution guidelines. | |
| License | |
| This project is licensed under the MIT License - see the LICENSE file for details. | |