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app.py

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+import gradio as gr
+import pickle
+import re
+import string
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from nltk.tokenize import word_tokenize
+import nltk
+nltk.download('punkt')
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+# ============ Load Models and Tokenizers ============
+with open("logreg_model.pkl", "rb") as f:
+    logreg_model = pickle.load(f)
+
+with open("nb_model.pkl", "rb") as f:
+    nb_model = pickle.load(f)
+
+with open("tfidf_vectorizer.pkl", "rb") as f:
+    tfidf_vectorizer = pickle.load(f)
+
+with open("glove_tokenizer.pkl", "rb") as f:
+    glove_tokenizer = pickle.load(f)
+
+model_glove = tf.keras.models.load_model("glove_model.h5")
+
+# ============ Constants ============
+MAX_LENGTH = 300
+stop_words = set(stopwords.words('english'))
+lemmatizer = WordNetLemmatizer()
+
+# ============ Preprocessing ============
+def clean_text(text):
+    text = str(text).lower()
+    text = re.sub(r'\[.*?\]', '', text)
+    text = re.sub(r'https?://\S+|www\.\S+', '', text)
+    text = re.sub(r'<.*?>+', '', text)
+    text = re.sub(f"[{re.escape(string.punctuation)}]", '', text)
+    text = re.sub(r'\n', ' ', text)
+    text = re.sub(r'\w*\d\w*', '', text)
+    text = text.replace('“', '').replace('”', '').replace("’", "'").replace("‘", "'")
+    text = re.sub(r"'s\b", '', text)
+    
+    tokens = word_tokenize(text)
+    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words and len(word) > 2]
+    return ' '.join(tokens)
+
+# ============ Prediction ============
+def predict_ensemble(text):
+    cleaned = clean_text(text)
+
+    # Check if cleaned text is too short
+    if len(cleaned.strip()) == 10:
+        return "Input too short to analyze."
+
+    # TF-IDF-based predictions
+    tfidf_vec = tfidf_vectorizer.transform([cleaned])
+    prob_nb = nb_model.predict_proba(tfidf_vec)[0][1]
+    prob_logreg = logreg_model.predict_proba(tfidf_vec)[0][1]
+
+    # GloVe prediction
+    glove_seq = glove_tokenizer.texts_to_sequences([cleaned])
+    glove_pad = pad_sequences(glove_seq, maxlen=MAX_LENGTH, padding='post', truncating='post')
+    prob_glove = model_glove.predict(glove_pad)[0][0]
+
+    # Weighted ensemble
+    ensemble_score = 0.55 * prob_nb + 0.1 * prob_logreg + 0.35 * prob_glove
+    label = "✅ Real News" if ensemble_score >= 0.45 else "❌ Fake News"
+
+    # Optional: Include probabilities
+    # Naive Bayes:
+    # Logistic Regression:
+    # GloVe Model:
+
+    explanation = f"""
+**Model 1** {prob_nb:.4f}  
+**Model 2** {prob_logreg:.4f}  
+**Model 3** {prob_glove:.4f}  
+**Ensemble Score:** {ensemble_score:.4f}  
+**Final Prediction:** {label}
+"""
+    return explanation
+
+# ============ Gradio Interface ============
+interface = gr.Interface(
+    fn=predict_ensemble,
+    inputs=gr.Textbox(lines=8, placeholder="Paste your news article here...", label="News Article"),
+    outputs=gr.Markdown(label="Prediction"),
+    title="📰 Fake News Detector",
+    description="This tool uses 3 models (Naive Bayes, Logistic Regression, GloVe-based Deep Learning) to classify news as real or fake using an ensemble method."
+    allow_flagging="never" 
+)
+
+if __name__ == "__main__":
+    interface.launch()
+
+

requirements.txt

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+gradio
+tensorflow
+scikit-learn
+nltk
+numpy