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app.py

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+import numpy as np
+import os
+import gradio as gr
+import xgboost as xgb
+import pickle
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+
+os.environ["WANDB_DISABLED"] = "true"
+
+label2id =  {
+    0: "negative",
+    1: "neutral",
+    2: "positive"
+  }
+# names of the files saved in step 2: Training
+
+model_file_name = "xgb_reg_dg.pkl"
+vectorizer_file_name = 'vectorizer_dg.pk'
+
+
+# load
+xgb_model_loaded = pickle.load(open(model_file_name, "rb"))
+vectorizer_loaded = pickle.load(open(vectorizer_file_name, "rb"))
+
+
+def predict_sentiment(predict_texts):
+    predictions_loaded = xgb_model_loaded.predict(vectorizer_loaded.transform([predict_texts]))
+    print(predictions_loaded)
+    return label2id[predictions_loaded[0]]
+
+
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs='text',
+    outputs=['text'],
+    title='Croatian Book reviews Sentiment Analysis',
+    examples= ["Volim kavu","Ne volim kavu"],
+    description='Get the positive/neutral/negative sentiment for the given input.'
+)
+
+interface.launch(inline = False)   

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9409399c00c3e769a42dec5f11f7860f50e5a2be87a9371687fca1bc9dc9af52
+size 578394