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-## 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.