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-# Sentiment Analysis App
-
-![Streamlit](https://img.shields.io/badge/Streamlit-%23FF4B4B?logo=streamlit&logoColor=white)
-![Python](https://img.shields.io/badge/Python-3.8%2B-blue)
-![License](https://img.shields.io/badge/License-MIT-green)
-
-> A clean, production-minded Streamlit app that reads text and returns generic & finance-aware sentiment labels and confidence scores.
-
----
-
-## Why this project
-
-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).
-
----
-
-## Quick visual 
-![Demo](assets/demo.gif)  
-
----
-
-## Project structure
-
-
-- **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  
-
----
-
-## Installation & Setup
-
-Below is a typical setup for development and running locally.
-
-1. **Clone the repository**
-
-   ```bash
-   git clone https://github.com/Ani-404/Sentiment-Analysis-App.git
-   cd Sentiment-Analysis-App
-   ```
-2. **(Optional) Create & activate a virtual environment**
-
-   ```bash
-   python3 -m venv venv
-   source venv/bin/activate
-   ```
-   
-3. **Install dependencies**
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-4. **Run the application locally**
-   ```bash
-   streamlit run app.py
-   ```
----
-
-
-   
-
-