initial commit

app.py

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+import streamlit as st
+from transformers import pipeline
+import nltk
+import re
+import string
+
+# Download necessary NLTK resources
+nltk.download('punkt')
+nltk.download('wordnet')
+
+# Load the sentiment analysis model
+sentiment_analyzer = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
+
+# Text preprocessing functions
+def remove_urls(text):
+    return re.sub(r'http[s]?://\S+', '', text)
+
+def remove_punctuation(text):
+    regular_punct = string.punctuation
+    return re.sub(r'[' + regular_punct + ']', '', text)
+
+def lower_case(text):
+    return text.lower()
+
+def lemmatize(text):
+    wordnet_lemmatizer = nltk.WordNetLemmatizer()
+    tokens = nltk.word_tokenize(text)
+    lemmatized_text = [wordnet_lemmatizer.lemmatize(w) for w in tokens]
+    return ' '.join(lemmatized_text)
+
+# Streamlit UI
+def main():
+    st.title("Sentiment Analysis")
+    st.write("Enter the text you'd like to analyze:")
+
+    user_input = st.text_area("Text Input", height=150)
+    if st.button("Analyze Sentiment"):
+        if user_input:
+            # Preprocess the text
+            text = remove_urls(user_input)
+            text = remove_punctuation(text)
+            text = lower_case(text)
+            text = lemmatize(text)
+
+            # Perform sentiment analysis
+            try:
+                result = sentiment_analyzer(text)
+                st.write(result)
+            except Exception as e:
+                st.error(f"Error analyzing sentiment: {str(e)}")
+        else:
+            st.error("Please enter some text to analyze.")
+
+if __name__ == "__main__":
+    main()