A clean, production-minded Streamlit app that reads text and returns generic & finance-aware sentiment labels and confidence scores.

Market language is different: the same phrase can mean different things in finance compared to casual chat. This app is designed to:

• prioritize clarity (human-readable outputs),
• be resilient in deployment (tips to avoid first-run timeouts),
• and be easy to extend (swap the model, add dashboards).

• app.py - main Streamlit application script
• requirements.txt - Python dependencies
• setup.sh - shell script for environment setup
• runtime.txt - runtime configuration (for deployment, e.g. Heroku)
• .devcontainer/ - config for VSCode / dev container setup
• Data/ - datasets, corpora, lexicons etc.
• finance/ - finance-specific modules, models, tools
• notebooks/- Jupyter notebooks used during experimentation / prototyping

Below is a typical setup for development and running locally.

1. Clone the repository

   git clone https://github.com/Ani-404/Sentiment-Analysis-App.git
   cd Sentiment-Analysis-App

2. (Optional) Create & activate a virtual environment

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the application locally

   streamlit run app.py