Update app.py

app.py

@@ -1,17 +1,37 @@
 import os
 os.system('pip install transformers')
 os.system('pip install torch')
-import streamlit as st
+
+# Import required libraries
+import torch
 import transformers
+import streamlit as st
+from transformers import pipeline
+
+# Initialize the Streamlit app
+st.set_page_config(layout="wide")
+st.header("Sentiment Analysis App")
+
+# Define the function for performing sentiment analysis
+def analyze_sentiment(text):
+    # Load the pre-trained sentiment analysis model and tokenizer
+    model = pipeline('sentiment-analysis')
+    
+    # Perform sentiment analysis on the input text
+    result = model(text)[0]['label']
+    
+    # Return the predicted sentiment label
+    return result
 
-classifier = transformers.pipeline("sentiment-analysis")
-txt = st.text_area(
-    "Text to analyze",
-    "It was the best of times, it was the worst of times, it was the age of "
-    "wisdom, it was the age of foolishness, it was the epoch of belief, it "
-    "was the epoch of incredulity, it was the season of Light, it was the "
-    "season of Darkness, it was the spring of hope, it was the winter of "
-    "despair, (...)",
-    )
+# Create the user input form
+col1, col2 = st.beta_columns(2)
+with col1:
+    text_input = st.text_area("Enter Text:", "", key="my_input")
+with col2:
+    submitted = st.form_submit_button("Submit")
 
-st.write(f'You wrote {len(txt)} characters.\n {classifier(txt)}')
+# Display the sentiment analysis results
+if submitted:
+    sentiment = analyze_sentiment(text_input)
+    st.subheader("Sentiment Analysis Result:")
+    st.write(f"**Predicted Sentiment:** {sentiment}")