Delete app.py

app.py

@@ -1,46 +0,0 @@
-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', map_location=torch.device('cpu')))
-    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[int(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)
-