Create app.py

app.py

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+import streamlit as st
+import pickle
+import tensorflow as tf
+import numpy as np
+import re
+import emoji
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.stem import WordNetLemmatizer
+from nltk.corpus import stopwords  
+
+# Ensure necessary downloads
+nltk.download("punkt")
+nltk.download("wordnet")
+nltk.download('stopwords')  
+
+lemmatizer = WordNetLemmatizer()
+stop_words = set(stopwords.words('english'))  
+
+def pre_process(x):
+    x = x.lower()
+    x = re.sub("<.*?>", "", x)
+    x = re.sub("http[s]?://.+?\\S+", "", x)
+    x = re.sub("[@#].+?\\S", "", x)
+    x = re.sub(r"\\_+", " ", x)
+    x = re.sub("^[A-Za-z.].*\\s-\\s", "", x)
+    x = emoji.demojize(x)
+    x = re.sub(":.*?:", "", x)
+    x = re.sub("[^a-zA-Z0-9\\s_]", "", x)
+    words = word_tokenize(x)
+    words = [word for word in words if word not in stop_words]
+    x = " ".join([lemmatizer.lemmatize(word) for word in words])
+    return x
+
+# Load the label encoder
+with open("label_encoder.pkl", "rb") as f:
+    label_encoder = pickle.load(f)
+
+# Load the text vectorization model
+text_vectorizer = tf.keras.models.load_model("news_tv_model.keras")
+
+# Load the news classification model
+news_model = tf.keras.models.load_model("news_model.keras")
+
+def predict_category(text):
+    # Preprocess the input text
+    processed_text = [pre_process(text[0])]
+    vectorized_text = text_vectorizer(processed_text)
+    # Predict category
+    prediction = news_model.predict(vectorized_text)
+    predicted_label_index = np.argmax(prediction, axis=1)[0]
+    predicted_label = label_encoder.inverse_transform([predicted_label_index])[0]
+    return predicted_label
+
+# Streamlit UI
+st.title("News Classification App")
+
+# User input
+user_text = st.text_area("Enter news text:")
+
+if st.button("Predict Category"):
+    if user_text.strip():
+        category = predict_category([user_text])
+        st.success(f"Predicted Category: {category}")
+    else:
+        st.warning("Please enter some text to classify.")