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---
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--
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```
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---
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title: My Streamlit Space
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emoji: 🚀
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colorFrom: indigo
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colorTo: purple
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sdk: streamlit
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app_file: app.py
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pinned: false
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---
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Simple Streamlit demo on Hugging Face Spaces.
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# Sentiment Analysis App
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> A clean, production-minded Streamlit app that reads text and returns generic & finance-aware sentiment labels and confidence scores.
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---
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## Why this project
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Market language is different: the same phrase can mean different things in finance compared to casual chat. This app is designed to:
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- prioritize clarity (human-readable outputs),
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- be resilient in deployment (tips to avoid first-run timeouts),
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- and be easy to extend (swap the model, add dashboards).
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---
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## Quick visual
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---
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## Project structure
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- **app.py** - main Streamlit application script
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- **requirements.txt** - Python dependencies
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- **setup.sh** - shell script for environment setup
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- **runtime.txt** - runtime configuration (for deployment, e.g. Heroku)
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- **.devcontainer/** - config for VSCode / dev container setup
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- **Data/** - datasets, corpora, lexicons etc.
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- **finance/** - finance-specific modules, models, tools
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- **notebooks/**- Jupyter notebooks used during experimentation / prototyping
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---
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## Installation & Setup
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Below is a typical setup for development and running locally.
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1. **Clone the repository**
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```bash
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git clone https://github.com/Ani-404/Sentiment-Analysis-App.git
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cd Sentiment-Analysis-App
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```
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2. **(Optional) Create & activate a virtual environment**
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```bash
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python3 -m venv venv
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source venv/bin/activate
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```
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3. **Install dependencies**
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```bash
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pip install -r requirements.txt
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```
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4. **Run the application locally**
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```bash
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streamlit run app.py
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```
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---
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