Create app.py

app.py

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+import re
+import emoji
+import nltk
+import numpy as np
+import streamlit as st
+from nltk.tokenize import word_tokenize
+from nltk.stem import WordNetLemmatizer
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+
+# Ensure necessary downloads
+nltk.download("punkt")
+nltk.download("wordnet")
+
+lemmatizer = WordNetLemmatizer()
+
+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)
+    x = " ".join([lemmatizer.lemmatize(word) for word in words])
+    return x
+
+# Load trained model
+model = load_model("best_rnn_model.h5")
+
+# Tokenizer (Ensure this matches the one used during training)
+MAX_LENGTH = 100  # Set this to the same max length used in training
+tokenizer = Tokenizer()  # Load your trained tokenizer here
+
+# Class labels
+class_labels = ['Sports', 'Business', 'SciTech', 'World']
+
+def predict_category(text):
+    processed_text = pre_process(text)
+    seq = tokenizer.texts_to_sequences([processed_text])
+    padded_seq = pad_sequences(seq, maxlen=MAX_LENGTH, padding='post')
+    prediction = model.predict(padded_seq)
+    predicted_label = class_labels[np.argmax(prediction)]
+    return predicted_label
+
+# Streamlit UI
+st.title("News Category Classifier")
+st.write("Enter a news headline or article snippet, and the model will predict its category.")
+
+user_input = st.text_area("Enter text here:")
+
+if st.button("Predict"):
+    if user_input.strip():
+        prediction = predict_category(user_input)
+        st.success(f"Predicted Category: {prediction}")
+    else:
+        st.warning("Please enter some text to classify.")