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eda.py

@@ -2,6 +2,8 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
+import nltk
+nltk.download('stopwords')
 from nltk.corpus import stopwords
 from wordcloud import WordCloud
 

prediction.py

@@ -17,25 +17,26 @@ def get_feeling(number):
   feeling = number_to_feeling.get(str(number), "Unknown feeling")
   return feeling
 
+with st.spinner("Loading the model, please wait..."):
+  the_model = tf.keras.models.load_model('model.keras', custom_objects={'KerasLayer': tf_hub.KerasLayer})
+
 def app():
   st.header('Prediction', divider='rainbow')
 
   user_input = st.text_input("Enter your text here:")
-  with st.spinner("Loading the model, please wait..."):
-    the_model = tf.keras.models.load_model('model.keras', custom_objects={'KerasLayer': tf_hub.KerasLayer})
-    if st.button('Predict', type="secondary"):
-      data = {
-        "text_processed": [
-            user_input
-        ]
-      }
-      df = pd.DataFrame(data)
-      with st.spinner("Making prediction..."):
-        # Replace with your preprocessing and prediction code
-        predictions = the_model.predict(df)
-        predicted_class = np.argmax(predictions, axis=1)
-        the_sentiment = predicted_class[0]
+  if st.button('Predict', type="secondary"):
+    data = {
+      "text_processed": [
+          user_input
+      ]
+    }
+    df = pd.DataFrame(data)
+    with st.spinner("Making prediction..."):
+      # Replace with your preprocessing and prediction code
+      predictions = the_model.predict(df)
+      predicted_class = np.argmax(predictions, axis=1)
+      the_sentiment = predicted_class[0]
 
-        st.write(f"We have predicted that the sentiment of this text is {get_feeling(the_sentiment)}")
-    else:
-      st.write("Click the button to predict!")
+      st.write(f"We have predicted that the sentiment of this text is {get_feeling(the_sentiment)}")
+  else:
+    st.write("Click the button to predict!")