https://github.com/sarthak-1408/techdome-solution-assignment
This Repository is only for assignment purpose that is provided by Techdome Solutions Private Limited for AI Engineer Role
https://github.com/sarthak-1408/techdome-solution-assignment
Last synced: 2 months ago
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This Repository is only for assignment purpose that is provided by Techdome Solutions Private Limited for AI Engineer Role
- Host: GitHub
- URL: https://github.com/sarthak-1408/techdome-solution-assignment
- Owner: Sarthak-1408
- Created: 2023-09-30T12:53:11.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-30T13:37:00.000Z (about 2 years ago)
- Last Synced: 2025-01-16T13:59:23.605Z (9 months ago)
- Language: Jupyter Notebook
- Size: 3.83 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Techdome-solution-assignment
This Repository is only for assignment purpose that is provided by Techdome Solutions Private Limited for AI Engineer Role
## Overview
This project implements a simple web application for sentiment analysis using a deep learning model. Users can enter a text review, and the model will predict whether the sentiment is positive or negative.
## Web App Demo
Check out the deployed web app: https://techdome-solution-assignment.streamlit.app/
## Demo Image of Web app
## Files and Directories
- `sentiment_analysis_model.h5`: The trained sentiment analysis model's weights and architecture.
- `tokenizer.pickle`: Serialized tokenizer used for text preprocessing.
- `app.py`: The main Streamlit web application script.
- `requirements.txt`: List of Python dependencies for your project.
- `Techdome_solution_assignment.ipynb`: Jypyter Notebook script for data prorcessing, model building and training.
## Usage
1. **Clone the repository:**
```bash
git clone https://github.com/Sarthak-1408/Techdome-solution-assignment
cd Techdome-solution-assignment
2. Install dependencies:
```bash
pip install -r requirements.txt
3. Run the Streamlit app:
```bash
streamlit run app.py
## Model Training
If you need to retrain the model, refer to the training script and data in your Jupyter notebook or Python script. Update the model file and tokenizer accordingly.