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app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import altair as alt
+from transformers import BertTokenizer, BertForSequenceClassification
+
+@st.cache_data
+def get_model():
+    tokenizer = BertTokenizer.from_pretrained('Dilwolf/Kakao_app-kr_sentiment')
+    model = BertForSequenceClassification.from_pretrained("Dilwolf/Kakao_app-kr_sentiment")
+    return tokenizer, model
+
+tokenizer, model = get_model()
+
+# Define the "How to Use" message
+how_to_use = """
+**How to Use**
+1. Enter text in the text area
+2. Click the 'Analyze' button to get the predicted sentiment of the input text
+"""
+
+# Functions
+def main():
+    st.title("Kakao App Review Sentiment Analysis using BERT")
+    st.subheader("Dilshod's Portfolio Project")
+
+    # Add the cover image
+    st.image("img/kakaotalk.png")
+
+    menu = ["Home", "About"]
+    choice = st.sidebar.selectbox("Menu", menu)
+
+    # Add the "How to Use" message to the sidebar
+    st.sidebar.markdown(how_to_use)
+
+    if choice == "Home":
+        st.subheader("Home")
+
+        with st.form(key="nlpForm"):
+            raw_text = st.text_area("Enter Text Here")
+            submit_button = st.form_submit_button(label="Analyze")
+
+        # Layout
+        col1, col2 = st.columns(2)
+        if submit_button and raw_text:
+            # Display balloons
+            st.balloons()
+            with col1:
+                st.info("Results")
+                # Tokenize the input text
+                inputs = tokenizer([raw_text], padding=True, truncation=True, max_length=512, return_tensors='pt')
+
+                # Make a forward pass through the model
+                outputs = model(**inputs)
+
+                # Get the predicted class and associated score
+                predicted_class = outputs.logits.argmax().item()
+                scores = outputs.logits.softmax(dim=1).detach().numpy()[0]
+
+                # Mapping of prediction to sentiment labels
+                sentiment_dict = {0: 'Negative', 1: 'Positive'}
+                sentiment_label = sentiment_dict[predicted_class]
+                confidence_level = scores[predicted_class]
+
+                # Display sentiment
+                st.write(f"Sentiment: {sentiment_label}, Confidence Level: {confidence_level:.2f}")
+
+                # Emoji and sentiment image
+                if predicted_class == 1:
+                    st.markdown("Sentiment: Positive :smiley:")
+                    st.image("img/positive_emoji.jpg")
+                else:
+                    st.markdown("Sentiment: Negative :angry:")
+                    st.image("img/negative_emoji.jpg")
+
+            # Create the results DataFrame
+            results_df = pd.DataFrame({
+                'Sentiment Class': ['Negative', 'Positive'],
+                'Score': scores
+            })
+
+            # Create the Altair chart
+            chart = alt.Chart(results_df).mark_bar(width=50).encode(
+                x="Sentiment Class",
+                y="Score",
+                color="Sentiment Class"
+            )
+
+            # Display the chart
+            with col2:
+                st.altair_chart(chart, use_container_width=True)
+                st.write(results_df)
+
+    else:
+        st.subheader("About")
+        st.write("This is a sentiment analysis NLP app developed by Dilshod for analyzing reviews for KakakTalk mobile app on Google Play Store. It uses a fine-tuned model to predict the sentiment of the input text. The app is part of a portfolio project to showcase my nlp skills and collaboration among developers.")
+
+if __name__ == "__main__":
+    main()
+

requirements.txt

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+streamlit
+torch
+transformers
+altair
+pandas