Create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from unidecode import unidecode
+import tensorflow as tf 
+import cloudpickle
+from transformers import DistilBertTokenizerFast
+import os
+
+def load_model():
+    interpreter = tf.lite.Interpreter(model_path=os.path.join("models/lang_detect_hf_distilbert.tflite"))
+    with open("models/lang_detect_labelencoder.bin", "rb") as model_file_obj:
+        label_encoder = cloudpickle.load(model_file_obj)
+        
+    model_checkpoint = "distilbert-base-multilingual-cased"
+    tokenizer = DistilBertTokenizerFast.from_pretrained(model_checkpoint)
+    return interpreter, label_encoder, tokenizer
+
+interpreter, label_encoder, tokenizer = load_model()
+
+def inference(text):
+    tflite_pred = "Can't Predict"
+    if text != "":
+        tokens = tokenizer(text, max_length=50, padding="max_length", truncation=True, return_tensors="tf")
+        # tflite model inference  
+        interpreter.allocate_tensors()
+        input_details = interpreter.get_input_details()
+        output_details = interpreter.get_output_details()[0]
+        attention_mask, input_ids = tokens['attention_mask'], tokens['input_ids']
+        interpreter.set_tensor(input_details[0]["index"], attention_mask)
+        interpreter.set_tensor(input_details[1]["index"], input_ids)
+        interpreter.invoke()
+        tflite_pred = interpreter.get_tensor(output_details["index"])[0]
+        tflite_pred_argmax = np.argmax(tflite_pred)
+        tflite_pred = f"{label_encoder.inverse_transform([tflite_pred_argmax])[0].upper()} ({str(np.round(tflite_pred[tflite_pred_argmax], 3))})"
+    return tflite_pred
+
+
+def main():
+    st.title("Language Detection")
+    lang_trained = 'eng, rus, ita, tur, epo, ber, deu, kab, fra, por, spa, hun, jpn, heb, ukr, nld, fin, pol, mkd, lit, cmn, mar, ces, dan'.upper()
+    st.write(f'Model is trained on the following languages  \n{lang_trained}')
+    review = st.text_area("Enter Text:", "", height=200)
+    if st.button("Submit"):
+        result = inference(review)
+        st.write(result)
+
+if __name__ == "__main__":
+    main()