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Dockerfile

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+# FROM centos:latest
+# Use the official Python base image with version 3.9
+FROM python:3.9
+
+WORKDIR /poem_classifier 
+
+# Copy the requirements file to the container
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install -r requirements.txt
+
+# Copy the application code to the container
+COPY . .
+# ENTRYPOINT ["./predict.py"]
+
+# Set the command to run the application
+CMD ["python", "predict.py"]

best_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0444cb1014afab6ba7c00849d430d950e4273fe6e92e6638590a98982f4b9bfe
+size 953531

predict.py

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+#!/usr/bin/python3
+import pickle
+# import numpy as np # linear algebra
+# import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+# import pandas as pd
+# import numpy as np
+# import re
+# import nltk
+# from nltk.corpus import stopwords
+# from nltk.stem import WordNetLemmatizer
+# from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer
+# from sklearn.decomposition import LatentDirichletAllocation
+# from sklearn.model_selection import train_test_split
+# from sklearn.naive_bayes import MultinomialNB
+# from sklearn.metrics import accuracy_score, confusion_matrix
+# from sklearn.linear_model import LogisticRegression
+# from sklearn.tree import DecisionTreeClassifier
+# from sklearn.ensemble import RandomForestClassifier
+# from sklearn.pipeline import Pipeline
+# from sklearn.model_selection import GridSearchCV
+# from sklearn.metrics import classification_report
+file_name = 'best_model.pkl'
+with open(file_name, 'rb') as file:
+    model = pickle.load(file)
+# ohe = joblib.load('state_ohe.pkl')
+class_mapping = ['Music', 'Death', 'Environment', 'Affection']
+
+class Profit:
+      
+    def __init__(self,data):
+        self.data = data
+        
+    def predict(self):
+        d_data = [data]
+        predict = model.predict(d_data)[0]
+
+        print(f"This prediction is: {class_mapping[predict-1]}\n")
+                
+if __name__ == "__main__":
+    print("************************")
+    print("Poem prediction")
+    print("************************\n\n")
+    data = input('Enter Poem: ')
+
+    obj = Profit(data)
+    obj.predict()

requirements.txt

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+pandas==2.0.2
+numpy==1.24.3
+nltk==3.8.1
+scikit-learn==1.3.0
+cloudpickle==2.2.1
+pickleshare==0.7.5