Create README.md

README.md

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+# Language Identifier
+---
+
+## OVERVIEW
+This project is a Flask web application that identifies the language of input text. It uses a machine learning model trained on text data to make predictions. The user inputs text into a form on the web app, and the app returns the predicted language.
+
+## SPECIFICATIONS
+- The data used for training is taken from Kaggle. It has 22 different languages.
+- The text in the dataset has tokenization, non alphanumeric characters removal and vectorization applied to it.
+- The model used for training has 4 layers with 27M params which is enough for getting high accuracy. Complex architectures couldn’t be used because of not sufficient GPUs.
+- Techniques like early stopping, learning rate decay and weight decay are used while training to get the most accurate results.
+- The metrics used for evaluation is accuracy, 97.89% of which is achieved.
+- I usually use Pytorch but this time I used Tensorflow because converting tokens into tensors crashed the GPU constantly.
+- The project uses Flask, a lightweight web framework for Python, to create the web application.
+- The input text is preprocessed before being fed into the model for prediction.
+  
+## USAGE
+
+```python
+def predict_language(text, model, cv, le):
+    cleaned_text = clean_text(text)
+    text_vectorized = cv.transform([cleaned_text])
+    prediction = model.predict(text_vectorized)
+    predicted_label = le.inverse_transform([np.argmax(prediction)])[0]  # Get the first element of the list
+    return predicted_label
+sentence = 'random text'
+predicted_label = predict_language(sentence, model, cv, le)
+print(predicted_label)