Done with readme

README.md

@@ -1,90 +1,11 @@
-# RateMyReview
-
-**RateMyReview** is a FastAPI-powered service that uses **zero-shot text classification** to automatically assign ratings (1–5 stars) to customer reviews. Instead of relying on pre-labeled datasets, the system leverages Hugging Face’s `facebook/bart-large-mnli` model to classify reviews into custom labels such as *worst experience*, *bad experience*, *average experience*, *good experience*, and *excellent experience*.
-
-### ✨ Features
-
-* 🚀 **Zero-Shot Classification** – no manual dataset labeling required
-* 📊 **Text-to-Rating Mapping** – converts review text into a numeric score (1–5)
-* ⚡ **FastAPI Backend** – lightweight and production-ready API
-* 🐳 **Dockerized** – ready to run in any containerized environment
-
-### 🔧 Tech Stack
-
-* [FastAPI](https://fastapi.tiangolo.com/) – API framework
-* [Transformers](https://huggingface.co/transformers/) – NLP models
-* [PyTorch](https://pytorch.org/) – deep learning backend
-* [Docker](https://www.docker.com/) – containerization
-
-### 📌 Example Usage
-
 ---
-title: RateMyReview
-emoji: "📝"
-colorFrom: indigo
+title: AI_review
+emoji: 📊
+colorFrom: yellow
 colorTo: blue
-sdk: streamlit
-sdk_version: "latest"
-app_file: app.py
+sdk: docker
 pinned: false
+short_description: An app that reviews as ratings 
 ---
 
-````markdown
-# RateMyReview
-
-**RateMyReview** is a FastAPI-powered service that uses **zero-shot text classification** to automatically assign ratings (1–5 stars) to customer reviews. Instead of relying on pre-labeled datasets, the system leverages Hugging Face’s `facebook/bart-large-mnli` model to classify reviews into custom labels such as *worst experience*, *bad experience*, *average experience*, *good experience*, and *excellent experience*.
-
-### ✨ Features
-
-* 🚀 **Zero-Shot Classification** – no manual dataset labeling required
-* 📊 **Text-to-Rating Mapping** – converts review text into a numeric score (1–5)
-* ⚡ **FastAPI Backend** – lightweight and production-ready API
-* 🐳 **Dockerized** – ready to run in any containerized environment
-
-### 🔧 Tech Stack
-
-* [FastAPI](https://fastapi.tiangolo.com/) – API framework
-* [Transformers](https://huggingface.co/transformers/) – NLP models
-* [PyTorch](https://pytorch.org/) – deep learning backend
-* [Docker](https://www.docker.com/) – containerization
-
-### 📌 Example Usage
-
-```json
-Input:  
-"This was the most disappointing purchase I've ever made."  
-
-Output:  
-{
-  "score": 1,
-  "label": "worst experience"
-}
-```
-
-### 🚀 Getting Started
-
-1. Clone the repo
-
-   ```bash
-   git clone https://github.com/<your-username>/RateMyReview.git
-   cd RateMyReview
-   ```
-
-2. Build and run with Docker
-
-   ```bash
-   docker build -t ratemyreview .
-   docker run -p 8000:8000 ratemyreview
-   ```
-
-3. Send a request
-
-   ```bash
-   curl -X POST "http://127.0.0.1:8000/" -H "Content-Type: application/json" -d '{"review": "Great product, I loved it!"}'
-   ```
-
----
-
-💡 This project demonstrates how **zero-shot learning** can be applied to real-world customer feedback analysis without a custom training dataset.
-
-````
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference