Adding text classifier example

.gitignore

@@ -1,2 +1,4 @@
 .ipynb_checkpoints
 .DS_Store
+text-classifier/__pycache__
+text-classifier/venv

README.md

@@ -1,5 +1,6 @@
-# Neural Networks Programming
+# Machine Learning Programming
 
 1. [Linear Regression](https://github.com/jpuri/neural-networks/tree/main/linear-regression)
 2. [Shallow Neural Network](https://github.com/jpuri/neural-networks/tree/main/shallow-neural-network)
 3. [Deep Neural Network](https://github.com/jpuri/neural-networks/tree/main/deep-neural-network)
+4. [Text Classification](https://github.com/jpuri/neural-networks/tree/main/text-classifier)

text-classifier/README.md

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+# Text Classification
+
+Project setup:
+
+```bash
+pip install -r requirements.txt
+python3 text_classifier.py
+```
+
+Open url like: `http://0.0.0.0:8000/classify?text=This is a test sentence.`
+
+The api is deployed at: [https://text-classifier-na9p.onrender.com/classify?text=This is a test sentence](https://text-classifier-na9p.onrender.com/classify?text=This is a test sentence.).

text-classifier/requirements.txt

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+annotated-types==0.7.0
+anyio==4.7.0
+click==8.1.7
+fastapi==0.115.6
+h11==0.14.0
+idna==3.10
+joblib==1.4.2
+nltk==3.9.1
+pydantic==2.10.3
+pydantic_core==2.27.1
+regex==2024.11.6
+sniffio==1.3.1
+starlette==0.41.3
+textblob==0.18.0.post0
+tqdm==4.67.1
+typing_extensions==4.12.2
+uvicorn==0.32.1

text-classifier/text_classifier.py

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+from fastapi import FastAPI
+from textblob import TextBlob
+from textblob.classifiers import NaiveBayesClassifier
+import nltk
+import uvicorn
+
+nltk.download('punkt')
+nltk.download('punkt_tab')
+
+data = [
+    ('I love my country.', 'pos'),
+    ('This is an amazing place!', 'pos'),
+    ('I do not like the smell of this place.', 'neg'),
+    ('I do not like this restaurant', 'neg'),
+    ('I am tired of hearing your nonsense.', 'neg'),
+    ("I always aspire to be like him", 'pos'),
+    ("It's a horrible performance.", "neg")
+]
+
+model = NaiveBayesClassifier(data)
+
+app = FastAPI()
+
+@app.get("/classify")
+async def read_root(text: str):
+    return { "class": model.classify(text) }
+
+if __name__ == "__main__":
+    uvicorn.run("text_classifier:app", host="0.0.0.0", port=8000, reload=False)