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1. Sentiment Classification
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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.
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2.Tumor Detection
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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.
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Getting Started
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Prerequisites
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Python 3.6 or higher
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Required packages: streamlit, numpy, cv2, PIL, tensorflow
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Pre-trained models: PP.pkl, BP.pkl, DP.keras, RN.keras, LS.keras, CN.keras
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Trained IMDb word index: Ensure the IMDb word index is available for sentiment classification.
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Installation
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Clone the repository: git clone https://github.com/yourusername/deep-prediction-hub.git
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Usage
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Access the application by opening the provided URL after running the Streamlit app.
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Choose between "Sentiment Classification" and "Tumor Detection" tasks.
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Sentiment Classification
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Enter a review in the text area.
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Select a model from the dropdown.
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Click "Submit" and then "Classify Sentiment."
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Tumor Detection
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Upload an image using the file uploader.
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Click "Detect Tumor" to perform tumor detection.
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Models
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Perceptron (PP.pkl): Perceptron-based sentiment classification model.
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Backpropagation (BP.pkl): Backpropagation-based sentiment classification model.
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DNN (DP.keras): Deep Neural Network sentiment classification model.
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RNN (RN.keras): Recurrent Neural Network sentiment classification model.
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LSTM (LS.keras): Long Short-Term Memory sentiment classification model.
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CNN (CN.keras): Convolutional Neural Network tumor detection model.
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Contributing
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Feel free to contribute by opening issues or submitting pull requests. Please follow the contribution guidelines.
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License
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This project is licensed under the MIT License - see the LICENSE file for details.
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---
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title: DeepLearning
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sdk: streamlit
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emoji: 📈
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colorFrom: blue
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colorTo: blue
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pinned: false
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---
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