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README.md

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----
-title: Very Basic Sentiment Analysis
-emoji: 💻
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Very Basic Sentiment Analysis API
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Overview](#overview)
+- [Dependencies](#dependencies)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Testing](#testing)
+- [Hugging Face Space](#hugging-face-space)
+- [Contributors](#contributors)
+- [License](#license)
+
+
+## Introduction
+This API utilizes a machine learning model to analyze text for sentiment, categorizing input as positive, negative, or neutral. It leverages a pre-trained BERT model from Hugging Face Transformers, integrated within a FastAPI framework to provide quick and reliable sentiment analysis.
+
+## Overview
+This project was developed to demonstrate the ability to deploy a machine learning model using FastAPI and Docker, making it accessible as a web API. The sentiment analysis model used is based on BERT, a transformer model pre-trained on a large corpus of text and fine-tuned for sentiment analysis.
+
+## Dependencies
+- **FastAPI**: A modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints.
+- **Docker**: A set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers.
+- **Pydantic**: Data validation and settings management using python type annotations.
+- **Hugging Face Transformers**: Provides thousands of pre-trained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. This project specifically utilizes the [Sentiment Analysis BERT model by MarieAngeA13](https://huggingface.co/MarieAngeA13/Sentiment-Analysis-BERT?text=I+like+you.+I+love+you) for analyzing text sentiment.
+- **pytest**: A framework that makes it easy to write simple tests yet scales to support complex functional testing.
+
+
+## Installation
+Follow these instructions to set up the API locally:
+
+### Clone the repository
+```bash
+git clone https://github.com/abdoolamunir/very-basic-sentiment-analysis.git
+cd very-basic-sentiment-analysis
+
+```
+
+### Build the Docker Container
+This command builds the Docker container, which includes installing all the necessary dependencies from 'requirements.txt'.
+```bash
+docker build -t sentiment-analysis-api .
+```
+
+### Run the Docker container
+```bash
+docker run -p 8000:8000 sentiment-analysis-api
+```
+
+## Usage
+After Launching the API, you can use it as follows:
+### Open Swagger UI
+```bash
+http://localhost:8000/docs
+```
+
+## Analyze text sentiment
+To analyze the text sentiment, send a POST requent:
+```bash
+curl -X 'POST' \
+  'http://localhost:8000/analyze' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{"text": "This product is great!"}'
+```
+
+## Example Response
+```json
+{
+  "result": [
+    {
+      "label": "POSITIVE",
+      "score": 0.9999
+    }
+  ]
+}
+```
+
+## Testing
+To run the tests, execute the following command:
+```bash
+pytest
+```
+## Hugging Face Space
+The API is also deployed on Hugging Face Spaces. You can access it here: []()
+
+## Contributors
+Abdullah Munir & anyone who wants to use this basic framework and add onto it :)
+
+## License
+This project is released under the Apache License 2.0. See the LICENSE file in the repository for more details.

main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, validator
+from transformers import pipeline
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Load the sentiment analysis pipeline
+sentiment_model = pipeline("text-classification", model="MarieAngeA13/Sentiment-Analysis-BERT")
+
+
+# Define a Pydantic model for the input data
+class Text(BaseModel):
+    text: str
+
+    @validator('text')
+    def must_not_be_blank(cls, value):
+        if not value.strip():  # Check if the text is not just whitespace
+            raise ValueError('Text must not be empty or just whitespace')
+        return value
+
+@app.get("/")
+def read_root():
+    return {"Hello": "Welcome to our Sentiment Analysis API, type '/docs' after the <URL> to access the Swagger UI"}
+
+@app.post("/analyze")
+def analyze(text: Text):
+    try:
+        # Process the text through the sentiment analysis model
+        result = sentiment_model(text.text)
+        return {"result": result}
+    except ValueError as ve:
+        # Handle validation errors, which occur when text is empty or just whitespace
+        raise HTTPException(status_code=400, detail=str(ve))
+    except Exception as e:
+        # Handle all other kinds of unexpected errors
+        raise HTTPException(status_code=500, detail="An error occurred during the analysis.")

requirements.txt

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+fastapi
+uvicorn
+pydantic
+transformers
+pytest
+httpx

test_api.py

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+from fastapi.testclient import TestClient
+from main import app
+client = TestClient(app)
+
+def test_positive_sentiment():
+    response = client.post("/analyze", json={"text": "I love this new product, it's amazing!"})
+    assert response.status_code == 200
+    assert "positive" in response.json()['result'][0]['label'].lower()
+
+def test_negative_sentiment():
+    response = client.post("/analyze", json={"text": "I hate this new product, it's terrible!"})
+    assert response.status_code == 200
+    assert "negative" in response.json()['result'][0]['label'].lower()
+
+def test_empty_text():
+    response = client.post("/analyze", json={"text": " "})
+    assert response.status_code == 400  
+    assert "must not be empty" in response.json()['detail'].lower()