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app.py

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+import gradio as gr
+import numpy as np
+from keras.models import load_model # type: ignore
+import joblib
+import nltk
+import pickle
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.stem import WordNetLemmatizer
+import re
+
+# Modeli ve TF-IDF vectorizer'ı yükle
+with open('disaster_model.pkl', 'rb') as f:
+    model = pickle.load(f)
+vectorizer = joblib.load("tfidf_vectorizer.pkl")
+
+# NLTK paketlerini indir
+nltk.download('stopwords')
+nltk.download('punkt')
+nltk.download('wordnet')
+
+# Preprocessing fonksiyonları
+stop_words = set(stopwords.words('english'))
+lemmatizer = WordNetLemmatizer()
+
+def preprocess_text(text):
+    text = text.lower()
+    text = re.sub(r'[^\w\s]', '', text)
+    text = re.sub(r'\s+', ' ', text)
+    text = re.sub(r'\d+', ' ', text)
+    tokens = word_tokenize(text)
+    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]
+    return ' '.join(tokens)
+
+def predict_sentiment(text):
+    text = preprocess_text(text)
+    text_vectorized = vectorizer.transform([text]).toarray()
+    prediction = model.predict(text_vectorized)
+    return 'Real Disaster' if prediction > 0.5 else 'Not Real Disaster'
+
+# Gradio arayüzü
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a tweet here..."),
+    outputs="text",
+    title="Tweet",
+    description="Enter a tweet and get the prediction (preal disaster or not real disaster)."
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

disaster_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:334eb2d1c2a38b57d4ef83a2151d3a76bd41bd56d602c2e8c85bfc7b42506a47
+size 2533612

requirements.txt

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+tensorflow
+keras
+gradio
+scikit-learn
+joblib
+nltk

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ebd1db5d2385e30a898e84996d76e08ded63c2065c361a9db875518b0052fbcb
+size 2084939