Delete streamlit_app.py/pages/SentimentDetection.py

streamlit_app.py/pages/SentimentDetection.py

@@ -1,100 +0,0 @@
-from os import path
-import streamlit as st
-
-# import pickle
-
-# from tensorflow import keras
-import tensorflow as tf
-import torch
-from torch import nn
-from transformers import BertModel, BertTokenizer
-
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-MODEL_NAME = 'bert-base-cased'
-
-# Build the Sentiment Classifier class 
-class SentimentClassifier(nn.Module):
-    
-    # Constructor class 
-    def __init__(self, n_classes):
-        super(SentimentClassifier, self).__init__()
-        self.bert = BertModel.from_pretrained(MODEL_NAME)
-        self.drop = nn.Dropout(p=0.3)
-        self.out = nn.Linear(self.bert.config.hidden_size, n_classes)
-    
-    # Forward propagaion class
-    def forward(self, input_ids, attention_mask):
-        _, pooled_output = self.bert(
-          input_ids=input_ids,
-          attention_mask=attention_mask,
-          return_dict=False
-        )
-        #  Add a dropout layer 
-        output = self.drop(pooled_output)
-        return self.out(output)
-
-# from keras_preprocessing.sequence import pad_sequences
-
-
-# def predict(ham_spam):
-#     model = load_model(r'test_HSmodel_r.h5')
-#     with open('tokenizer.pickle','rb') as handle:
-#         tokenizer = pickle.load(handle)
-#         tokenizer.fit_on_texts(ham_spam)
-#         x_1 = tokenizer.texts_to_sequences([ham_spam])
-#         x_1 = pad_sequences(x_1, maxlen=525)
-#         predictions = model.predict(x_1)[0][0]
-#         return predictions
-
-MODEL_PATH = path.join(path.dirname(__file__), "bert_model.h5")
-
-
-@st.cache_resource
-def load_model_and_tokenizer():
-    model = SentimentClassifier(3)
-    model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device('cpu')))
-    model.eval()
-    return model, BertTokenizer.from_pretrained('bert-base-cased')
-
-
-def predict(content):
-    model, tokenizer = load_model_and_tokenizer()
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    encoded_review = tokenizer.encode_plus(
-        content,
-        max_length=160,
-        add_special_tokens=True,
-        return_token_type_ids=False,
-        pad_to_max_length=True,
-        return_attention_mask=True,
-        return_tensors="pt",
-    )
-
-    input_ids = encoded_review["input_ids"].to(device)
-    attention_mask = encoded_review["attention_mask"].to(device)
-
-    output = model(input_ids, attention_mask)
-    _, prediction = torch.max(output, dim=1)
-
-    class_names = ["negative", "neutral", "positive"]
-
-    return class_names[prediction]
-
-
-def main():
-    # giving a title to our page
-    st.title("Sentiment detection")
-    contents = st.text_area("Please enter reviews/sentiment/setences/contents:")
-
-    prediction = ""
-
-    # Create a prediction button
-    if st.button("Analyze Spam Detection Result"):
-        prediction = predict(contents)
-        st.success(prediction)
-
-
-if __name__ == "__main__":
-    main()