Create app.py

app.py

@@ -0,0 +1,53 @@
+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 AlbertTokenizerFast
+import os
+
+def load_model():
+    interpreter = tf.lite.Interpreter(model_path=os.path.join("models/albert_sentiment_analysis.tflite"))
+    with open("models/sentiment_preprocessor_labelencoder.bin", "rb") as model_file_obj:
+        text_preprocessor, label_encoder = cloudpickle.load(model_file_obj)
+        
+    model_checkpoint = "albert-base-v2"
+    tokenizer = AlbertTokenizerFast.from_pretrained(model_checkpoint)
+    return interpreter, text_preprocessor, label_encoder, tokenizer
+
+interpreter, text_preprocessor, label_encoder, tokenizer = load_model()
+
+def inference(text):
+    tflite_pred = "Can't Predict"
+    text = text_preprocessor.preprocess(pd.Series(text))[0]
+    if text != "this is an empty message":
+        tokens = tokenizer(text, max_length=150, 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])} ({tflite_pred[tflite_pred_argmax]})"
+    return tflite_pred
+
+
+def main():
+    st.title("Sentiment Analysis App")
+    review = st.text_area("Enter Review:", "")
+    if st.button("Submit"):
+        # result = "Can't Predict"
+        # if len(review.strip()) > 0:
+        result = inference(review)
+        if result.find("positive") >=0 :
+            st.success(f"{result}")
+        else:
+            st.error(f"{result}")
+
+if __name__ == "__main__":
+    main()