configuring gunicorn timeout length

Dockerfile

@@ -18,4 +18,4 @@ RUN mkdir -p /code/huggingface_cache && chmod -R 777 /code/huggingface_cache
 ENV HF_HOME=/code/huggingface_cache
 
 # Run the application
-CMD ["sh", "-c", "gunicorn -b 0.0.0.0:7860 app:app"]
+CMD ["sh", "-c", "gunicorn -b 0.0.0.0:7860 --timeout 300 app:app"]

app.py

@@ -5,8 +5,9 @@ app = Flask(__name__)
 
 # Load data and train the model globally
 df = model.load_data('AI_Human.csv')  # Make sure this path is correct
-X_train, X_test, y_train, y_test = model.split_data(df)
-pipeline = model.create_pipeline(X_train, y_train)
+x_train, x_test, y_train, y_test = model.split_data(df)
+pipeline = model.create_pipeline(x_train, y_train)
+
 
 @app.route('/', methods=['GET', 'POST'])
 def home():
@@ -17,5 +18,6 @@ def home():
         return jsonify({'classification': prediction})
     return render_template('home.html')
 
+
 if __name__ == '__main__':
     app.run(debug=True)

model.py

@@ -5,32 +5,37 @@ from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
 from sklearn.naive_bayes import MultinomialNB
 from sklearn.metrics import accuracy_score, classification_report
 
+
 def remove_tags(text):
     tags = ['\n', '\'']
     for tag in tags:
         text = text.replace(tag, '')
     return text
 
+
 def load_data(filepath):
     df = pd.read_csv(filepath)
     df['text'] = df['text'].apply(remove_tags)
     return df
 
+
 def split_data(df):
     y = df['generated']
-    X = df['text']
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-    return X_train, X_test, y_train, y_test
+    x = df['text']
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
+    return x_train, x_test, y_train, y_test
 
-def create_pipeline(X_train, y_train):
+
+def create_pipeline(x_train, y_train):
     pipeline = Pipeline([
         ('count_vectorizer', CountVectorizer()),
         ('tfidf_transformer', TfidfTransformer()),
         ('classifier', MultinomialNB())
     ])
-    pipeline.fit(X_train, y_train)
+    pipeline.fit(x_train, y_train)
     return pipeline
 
+
 def predict_text(text, pipeline):
     processed_text = remove_tags(text)
     prediction = pipeline.predict([processed_text])[0]

requirements.txt

@@ -1,7 +1,5 @@
 flask
 gunicorn
 pandas
-torch==1.10.0
-tensorflow==2.8.0
 scikit-learn
 transformers