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app-sa-nb.py

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+
+import streamlit as st
+import pickle
+
+# Load the model
+with open('sentiment_pipeline.pkl', 'rb') as file:
+    loaded_model = pickle.load(file)
+
+# Streamlit app title
+st.title("Sentiment Analysis App")
+
+# Text input for user to enter a sentence
+user_input = st.text_input("Enter a sentence:")
+
+# Predict sentiment when user inputs text
+if user_input:
+    prediction = loaded_model.predict([user_input])[0]
+    st.write("Prediction:", "Positive" if prediction == 1 else "Negative")

requirements.txt

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+pandas
+scikit-learn
+streamlit
+numpy

sentiment_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f687518c6577551d2d462a45e12ed30f4838e13f8da9b8db1f8abb29d2dcc82
+size 4756342