Create app.py

app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import torch
+import numpy as np
+import transformers 
+import pickle
+
+def load_model():
+    model_finetuned = transformers.AutoModel.from_pretrained(
+        "nghuyong/ernie-2.0-base-en",
+        output_attentions = False,
+        output_hidden_states = False
+    )
+    model_finetuned.load_state_dict(torch.load('ErnieModel_imdb.pt'))
+    tokenizer = transformers.AutoTokenizer.from_pretrained("nghuyong/ernie-2.0-base-en")
+    return model_finetuned, tokenizer
+
+def preprocess_text(text_input, max_len, tokenizer):
+    input_tokens = tokenizer(
+        text_input, 
+        return_tensors='pt', 
+        padding=True, 
+        max_length=max_len,
+        truncation = True
+        )
+    return input_tokens
+
+def predict_sentiment(model, input_tokens):
+    id2label = {0: "NEGATIVE", 1: "POSITIVE"}
+    output = model(**input_tokens).pooler_output.detach().numpy()
+    with open('LogReg_imdb_Ernie.pkl', 'rb') as file:
+        cls = pickle.load(file)
+    result = id2label[cls.predict(output)]
+    return result
+
+st.title('Text sentiment analysis by ErnieModel')
+
+max_len = st.slider('Maximum word length', 0, 500, 250)
+
+text_input = st.text_input("Enter some text about movie")
+model, tokenizer = load_model()
+
+if text_input:
+    input_tokens = preprocess_text(text_input, max_len, tokenizer)
+    output = predict_sentiment(model, input_tokens)
+    st.write(output)
+