Update app.py

app.py

@@ -1,79 +1,81 @@
-import streamlit as st
-import pickle
-import numpy as np
-import os
-from tensorflow.keras.models import load_model
-import numpy as np
-import pandas as pd
-import re
-import nltk
-from nltk.stem import WordNetLemmatizer
-from nltk.tokenize import word_tokenize
-import matplotlib.pyplot as plt
-import seaborn as sns
-model = load_model('best_model.keras')
-# Load the tokenizer
-with open('tokenizer.pkl' ,'rb') as f:
-    tokenizer = pickle.load(f)
-
-# Load the label encoder
-with open('label_encoder.pkl', 'rb') as f:
-    label_encoder = pickle.load(f)
-
-# Load max_length
-with open('max_length.pkl', 'rb') as f:
-    max_length = pickle.load(f)
-
-# Load stop words
-with open('stop_words.pkl', 'rb') as f:
-    stop_words = pickle.load(f)
-
-lemmatizer = WordNetLemmatizer()
-def preprocess_text(text):
-    text = str(text)
-    text = text.lower()
-    text = re.sub(r'[^a-z\s]', '', text)
-    words = text.split()
-    st_words = stop_words
-    words = [word for word in words if word not in stop_words]
-    words = [lemmatizer.lemmatize(word) for word in words]
-    text = ' '.join(words)
-    return text
-def classify_text(text):
-    text = preprocess_text(text)
-    seq = tokenizer.texts_to_sequences([text])
-    padded_seq = np.pad(seq, ((0, 0), (0, max_length - len(seq[0]))), mode='constant')
-
-    prediction = model.predict(padded_seq)
-    predicted_label_index = np.argmax(prediction, axis=1)[0]
-    predicted_label = label_encoder.inverse_transform([predicted_label_index])[0]
-    categories = predicted_label.split('|')
-    
-    if len(categories) == 3:
-        main_category = categories[0]
-        sub_category = categories[1]
-        lowest_category = categories[2]
-    else:
-        main_category = "Unknown"
-        sub_category = "Unknown"
-        lowest_category = "Unknown"
-    return main_category, sub_category, lowest_category
-
-
-# Streamlit UI
-def main():
-    st.title("Text Classifier")
-    
-    # Text input
-    user_input = st.text_input("Enter text to classify")
-    
-    if st.button("Classify"):
-        if user_input:
-            # Classify input text
-           main_category, sub_category, lowest_category = classify_text(user_input)
-           st.success(f"Main Category: {main_category}, Sub Category: {sub_category}, Lowest Category: {lowest_category}")
-        else:
-            st.warning("Please enter some text.")
-
-if __name__ == '__main__':
+import streamlit as st
+import pickle
+import numpy as np
+import os
+from tensorflow.keras.models import load_model
+import numpy as np
+import pandas as pd
+import re
+import nltk
+from nltk.stem import WordNetLemmatizer
+from nltk.tokenize import word_tokenize
+import matplotlib.pyplot as plt
+import seaborn as sns
+import nltk
+nltk.download('wordnet')
+model = load_model('best_model.keras')
+# Load the tokenizer
+with open('tokenizer.pkl' ,'rb') as f:
+    tokenizer = pickle.load(f)
+
+# Load the label encoder
+with open('label_encoder.pkl', 'rb') as f:
+    label_encoder = pickle.load(f)
+
+# Load max_length
+with open('max_length.pkl', 'rb') as f:
+    max_length = pickle.load(f)
+
+# Load stop words
+with open('stop_words.pkl', 'rb') as f:
+    stop_words = pickle.load(f)
+
+lemmatizer = WordNetLemmatizer()
+def preprocess_text(text):
+    text = str(text)
+    text = text.lower()
+    text = re.sub(r'[^a-z\s]', '', text)
+    words = text.split()
+    st_words = stop_words
+    words = [word for word in words if word not in stop_words]
+    words = [lemmatizer.lemmatize(word) for word in words]
+    text = ' '.join(words)
+    return text
+def classify_text(text):
+    text = preprocess_text(text)
+    seq = tokenizer.texts_to_sequences([text])
+    padded_seq = np.pad(seq, ((0, 0), (0, max_length - len(seq[0]))), mode='constant')
+
+    prediction = model.predict(padded_seq)
+    predicted_label_index = np.argmax(prediction, axis=1)[0]
+    predicted_label = label_encoder.inverse_transform([predicted_label_index])[0]
+    categories = predicted_label.split('|')
+    
+    if len(categories) == 3:
+        main_category = categories[0]
+        sub_category = categories[1]
+        lowest_category = categories[2]
+    else:
+        main_category = "Unknown"
+        sub_category = "Unknown"
+        lowest_category = "Unknown"
+    return main_category, sub_category, lowest_category
+
+
+# Streamlit UI
+def main():
+    st.title("Text Classifier")
+    
+    # Text input
+    user_input = st.text_input("Enter text to classify")
+    
+    if st.button("Classify"):
+        if user_input:
+            # Classify input text
+           main_category, sub_category, lowest_category = classify_text(user_input)
+           st.success(f"Main Category: {main_category}, Sub Category: {sub_category}, Lowest Category: {lowest_category}")
+        else:
+            st.warning("Please enter some text.")
+
+if __name__ == '__main__':
     main()