sentiment analysis app

app.py

@@ -0,0 +1,17 @@
+from engine import SentimentAnalyzer
+import streamlit as st
+
+
+# Load the sentiment analysis model from Hugging Face
+sentiment_analysis = SentimentAnalyzer()
+
+# Define the Streamlit app interface
+st.title("User Sentiment Analysis")
+
+sentence = st.text_input("Enter a sentence:")
+
+# Perform sentiment analysis on the input sentence
+if sentence:
+    label = sentiment_analysis.get_sentiment(sentence)
+    # Display the sentiment analysis result to the user
+    st.write(f"Sentiment analysis result: {label}")

engine.py

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+from transformers import pipeline
+
+
+class SentimentAnalyzer:
+    """Class for analyzing the sentiment of sentences
+    """
+
+    def __init__(self) -> None:
+        """initializes the class with sentiment analysis pipeline using the distilbert-base-uncased-finetuned-sst-2-english model
+        """
+        self.analyzer = pipeline(
+            "sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+    def score_sentiment(self, sentence: str) -> float:
+        """Uses the analyzer to analyze the sentiment of the provided sentence
+
+        Parameters
+        ----------
+        sentence : str
+            a short sentence to be analyzed
+
+        Returns
+        -------
+        float
+            score of the sentiment from 0 to 1. Below 0.5 is negative, above is positive. 0.5 is neutral
+        """
+        return self.analyzer(sentence)[0]
+
+    def get_sentiment(self, sentence: str) -> str:
+        """returns the label of the sentiment provided
+
+        Parameters
+        ----------
+        sentence : str
+            a short sentence to be analyzed
+
+        Returns
+        -------
+        str
+            label of the sentiment wether it is positive, negative, or neutral
+        """
+        sentiment_score = self.score_sentiment(sentence)
+        return sentiment_score['label']
+
+
+if __name__ == "__main__":
+    sentence = "I ...  you"
+    sentiment_analyzer = SentimentAnalyzer()
+    print(sentiment_analyzer.get_sentiment(sentence))