Delete streamlit_app.py/pages/Sentiment_Detection.py

streamlit_app.py/pages/Sentiment_Detection.py

@@ -1,117 +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"
-MODEL_PATH = path.join(path.dirname(__file__), "bert_model.h5")
-
-
-# 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)
-
-
-@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():
-    st.set_page_config(page_title="Sentiment Detection", page_icon="📝")
-
-    # giving a title to our page
-    st.title("Sentiment detection")
-    contents = st.text_area(
-        "Please enter reviews/sentiment/setences/contents:",
-        placeholder="Enter your text here",
-        height=200,
-    )
-
-    prediction = ""
-
-    # Create a prediction button
-    if st.button("Analyze Sentiment"):
-        stripped = contents.strip()
-        if not stripped:
-            st.error("Please enter some text.")
-            return
-
-        prediction = predict(contents)
-        if prediction == "positive":
-            st.success("This is positive 😄")
-        elif prediction == "negative":
-            st.error("This is negative 😟")
-        else:
-            st.warning("This is neutral 🙂")
-
-    upload_file = st.file_uploader("Or upload a file", type=["txt"])
-    if upload_file is not None:
-        contents = upload_file.read().decode("utf-8")
-
-        for line in contents.splitlines():
-            line = line.strip()
-            if not line:
-                continue
-
-            prediction = predict(line)
-            if prediction == "positive":
-                st.success(line + "\n\nThis is positive 😄")
-            elif prediction == "negative":
-                st.error(line + "\n\nThis is negative 😟")
-            else:
-                st.warning(line + "\n\nThis is neutral 🙂")
-
-
-if __name__ == "__main__":
-    main()