Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import pickle
+import numpy as np
+import pandas as pd
+
+# Load Model
+model = tf.keras.models.load_model("news_classification_rnn.h5")
+
+# Load Preprocessing Function
+with open("preprocessing.pkl", "rb") as f:
+    clean_text = pickle.load(f)
+
+# Load TF-IDF Vectorizer
+with open("tfidf_vectorizer.pkl", "rb") as f:
+    vectorizer = pickle.load(f)import streamlit as st
+import tensorflow as tf
+import pickle
+import numpy as np
+import pandas as pd
+
+# Load Model
+model = tf.keras.models.load_model("news_classification_rnn.h5")
+
+# Load Preprocessing Function
+with open("preprocessing.pkl", "rb") as f:
+    clean_text = pickle.load(f)
+
+# Load TF-IDF Vectorizer
+with open("vectorizer.pkl", "rb") as f:
+    vectorizer = pickle.load(f)
+
+# Define News Categories
+news_categories = ["World", "Sports", "Business", "Sci/Tech"]
+
+# Streamlit UI
+st.title("📰 News Classification with Simple RNN + TF-IDF")
+st.write("Enter a news headline to predict its category.")
+
+user_input = st.text_area("Enter News Text:", "")
+
+if st.button("Classify"):
+    if user_input:
+        # Preprocess Input
+        processed_text = clean_text(user_input)
+
+        # Convert text to vector
+        text_vector = vectorizer.transform([processed_text]).toarray()
+
+        # Prediction
+        prediction = model.predict(text_vector)
+        category = np.argmax(prediction)
+
+        st.success(f"Predicted Category: {news_categories[category]}")
+    else:
+        st.warning("Please enter a news headline.")
+
+
+# Define News Categories
+news_categories = ["World", "Sports", "Business", "Sci/Tech"]
+
+# Streamlit UI
+st.title("📰 News Classification with Simple RNN + TF-IDF")
+st.write("Enter a news headline to predict its category.")
+
+user_input = st.text_area("Enter News Text:", "")
+
+if st.button("Classify"):
+    if user_input:
+        # Preprocess Input
+        processed_text = clean_text(user_input)
+
+        # Convert text to vector
+        text_vector = vectorizer.transform([processed_text]).toarray()
+
+        # Prediction
+        prediction = model.predict(text_vector)
+        category = np.argmax(prediction)
+
+        st.success(f"Predicted Category: {news_categories[category]}")
+    else:
+        st.warning("Please enter a news headline.")