Delete app.py

app.py

@@ -1,69 +0,0 @@
-import re
-import emoji
-import nltk
-import numpy as np
-import streamlit as st
-import pickle  # To load the tokenizer
-
-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
-
-# Ensure necessary downloads
-nltk.download("punkt")
-nltk.download("wordnet")
-nltk.download("omw-1.4")
-nltk.download("averaged_perceptron_tagger")
-
-lemmatizer = WordNetLemmatizer()
-
-# Function to preprocess text
-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")
-
-# Load the same tokenizer used during training
-with open("tokenizer.pickle", "rb") as handle:
-    tokenizer = pickle.load(handle)
-
-# Maximum length (must match training settings)
-MAX_LENGTH = 100  
-
-# Class labels
-class_labels = ['Sports', 'Business', 'SciTech', 'World']
-
-# Function to predict category
-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.")