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streamlit_app.py/Homepage.py

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+import streamlit as st
+
+st.set_page_config(
+page_title="Sentiment Detection"
+)
+   
+st.title("Sentiment Detection")
+    
+st.sidebar.success("Select a page above.")
+
+st.header("The Need for Sentiment Detection")
+st.text("""
+        Spam detection algorithms are used to detect and filter junk and spam emails with a high level of accuracy.
+        It is said that around half of all emails are spam, depending on the user. These emails can include scams or viruses intended to cause harm.
+        """)
+
+st.header("Data Source")
+st.text("""
+        Data Source: Preprocessed TREC 2007 Public Corpus Dataset. 
+        Link: https://www.kaggle.com/datasets/imdeepmind/preprocessed-trec-2007-public-corpus-dataset
+        
+        """)

streamlit_app.py/pages/SentimentDetection.py

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+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)
+
+        if prediction < 0.5:
+            st.success(prediction)
+        elif prediction > 0.5:
+            st.success(prediction)
+
+
+if __name__ == "__main__":
+    main()

streamlit_app.py/pages/bert_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f0e320ff87ab99bcb76ed153f14d6973dfa7bd1570d022c6d8bee1e496323e7
+size 433339657