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app.py

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+import gradio as gr
+import re
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+import joblib
+
+import nltk
+
+nltk.download('punkt')
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+
+tfidf_vectorizer = joblib.load('tfidf_vectorizer.pkl')
+classifier = joblib.load('classifier.pkl')
+
+# Preprocess function
+def preprocess_text(text):
+    # Text Cleaning
+    text = re.sub(r'[^\w\s]', '', text)
+
+    # Tokenization
+    tokens = word_tokenize(text)
+
+    # Stop Word Removal
+    stop_words = set(stopwords.words('english'))
+    tokens = [token for token in tokens if token.lower() not in stop_words]
+
+    # Lemmatization
+    lemmatizer = WordNetLemmatizer()
+    tokens = [lemmatizer.lemmatize(token) for token in tokens]
+
+    # Join tokens back to text
+    preprocessed_text = ' '.join(tokens)
+
+    return preprocessed_text
+
+# Gradio Function
+def classify_threat(text):
+    # Preprocess input text
+    preprocessed_text = preprocess_text(text)
+    
+    # Convert to TF-IDF vector
+    tfidf_text = tfidf_vectorizer.transform([preprocessed_text])
+    # Predict threat
+    threat = classifier.predict(tfidf_text)
+    return threat[0]
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_threat,
+    inputs="text",
+    outputs="text",
+    title="Threat Classification",
+    description="Enter a text description to classify the threat (e.g., oil, chemical).",
+)
+
+# Launch the interface
+iface.launch()

classifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9189d53b69f0d9ff501d0a51aa9ab344b2455e03bf347653fb84ed075f788f3b
+size 386759

requirements.txt

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+gradio 
+scikit-learn==1.2.2
+numpy
+nltk

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8302231cc4ed8b8ddda20c6dffc6993b97f47a7136a4eedf6795817f30928358
+size 345649