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eda.py

@@ -2,8 +2,8 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-# from nltk.corpus import stopwords
-# from wordcloud import WordCloud
+from nltk.corpus import stopwords
+from wordcloud import WordCloud
 
 def app():
   df_original = pd.read_csv("data.csv", delimiter=";")
@@ -15,15 +15,15 @@ def app():
   st.header('Exploratory Data Analysis', divider='rainbow')
   eda_list = ["Text Length Distribution", "Sentiment Distribution", "Word Clouds", "Stopwords Boxplot Distributions"]
   val = st.sidebar.radio("Choose plot to show", eda_list)
-  # stop_words = set(stopwords.words('english'))
-  # def plot_wordcloud(sentiment):
-  #   text = ' '.join(df[df['feeling'] == sentiment]['text'])
-  #   wordcloud = WordCloud(stopwords=stop_words, background_color='white').generate(text)
-  #   plt.figure(figsize=(10, 6))
-  #   plt.imshow(wordcloud, interpolation='bilinear')
-  #   plt.axis('off')
-  #   plt.title(f"Word Cloud for {sentiment} Sentiment")
-  #   st.pyplot(plt)
+  stop_words = set(stopwords.words('english'))
+  def plot_wordcloud(sentiment):
+    text = ' '.join(df[df['feeling'] == sentiment]['text'])
+    wordcloud = WordCloud(stopwords=stop_words, background_color='white').generate(text)
+    plt.figure(figsize=(10, 6))
+    plt.imshow(wordcloud, interpolation='bilinear')
+    plt.axis('off')
+    plt.title(f"Word Cloud for {sentiment} Sentiment")
+    st.pyplot(plt)
   if val == "Text Length Distribution":
     # Plot distribution
     st.header('Text Length Distribution')
@@ -53,12 +53,12 @@ def app():
     st.pyplot(plt)
     st.write("Insight: surprise sentiment has the lowest value of around 900 data")
   elif val == "Word Clouds":
-    # plot_wordcloud('joy')
-    # plot_wordcloud('sadness')
-    # plot_wordcloud('anger')
-    # plot_wordcloud('love')
-    # plot_wordcloud('surprise')
-    # plot_wordcloud('fear')
+    plot_wordcloud('joy')
+    plot_wordcloud('sadness')
+    plot_wordcloud('anger')
+    plot_wordcloud('love')
+    plot_wordcloud('surprise')
+    plot_wordcloud('fear')
     pass
   elif val == "Stopwords Boxplot Distributions":
     temp_b = df.copy()

prediction.py

@@ -14,7 +14,6 @@ number_to_feeling = {
 }
 
 def get_feeling(number):
-  # Convert the number to string to match the keys in the dictionary
   feeling = number_to_feeling.get(str(number), "Unknown feeling")
   return feeling
 
@@ -22,18 +21,21 @@ def app():
   st.header('Prediction', divider='rainbow')
 
   user_input = st.text_input("Enter your text here:")
-  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)
-    predictions = the_model.predict(df)
-    predicted_class = np.argmax(predictions, axis=1)
-    the_sentiment = predicted_class[0]
+  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]
 
-    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!")