Upload 4 files

BoW.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4adb2a9af8176082ebae13ade8305d3f0a97a710e6779a52bdfe8e98d38f192
+size 188821720

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import numpy as np
+from keras.models import load_model # type: ignore
+import joblib
+import nltk
+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
+model = load_model("BoW.h5", compile=False)
+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 'Positive' if prediction > 0.5 else 'Negative'
+
+# Gradio arayüzü
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a movie review here..."),
+    outputs="text",
+    title="Movie Review Sentiment Analysis",
+    description="Enter a movie review and get the sentiment prediction (positive or negative)."
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

@@ -0,0 +1,6 @@
+tensorflow
+keras
+gradio
+scikit-learn
+joblib
+nltk

tfidf_vectorizer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e69679f08527102bbf0299691c988bfb03ce8e6c2bbe3090d40471235a7d60d3
+size 4312540