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Machine Learning model to predict the genres of a movie from its summary
from flask import Flask, request
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
from sklearn.pipeline import Pipeline
import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from joblib import dump, load
import pickle
from io import StringIO
import numpy as np
import pandas as pd
import os
from sklearn.ensemble import RandomForestClassifier
app = Flask(__name__)
clf_path = "clf.joblib"
binarizer_path = "binary_classes.pickle"
def clean(X):"stopwords")
stop_words = stopwords.words("english")
ps = PorterStemmer()
def stem_sentence(sentence):
return " ".join(
[ps.stem(word) for word in sentence.split() if word not in stop_words]
X = [stem_sentence(sentence) for sentence in X]
return X
@app.route("/genres/train", methods=["POST"])
def train():
Post a CSV with header movie_id,synopsis,genres.
where genres is a space-separated list of movie genres.
Get the training result
bytes_data =
s = str(bytes_data, "utf-8")
string_data = StringIO(s)
df = pd.read_csv(string_data)
genres = df.genres.values
genre_lists = [line.split() for line in genres]
X_train = df.synopsis.values
multilabel_binarizer = MultiLabelBinarizer().fit(genre_lists)
binary_classes = multilabel_binarizer.classes_
with open(binarizer_path, "wb") as handle:
pickle.dump(binary_classes, handle, protocol=pickle.HIGHEST_PROTOCOL)
y_train = multilabel_binarizer.transform(genre_lists)
rf = RandomForestClassifier()
lr = LogisticRegression()
multi_label_clf = Pipeline(
("vect", CountVectorizer()),
("tfidf", TfidfTransformer()),
("clf", OneVsRestClassifier(rf)),
), y_train)
dump(multi_label_clf, clf_path)
return "training successful!"
@app.route("/genres/predict", methods=["POST"])
def predict():
Post a CSV with header movie_id,synopsis.
Get a CSV with header movie_id,predicted_genres,
where predicted_genres is a space-separated list of the top 5 movie genres.
bytes_data =
s = str(bytes_data, "utf-8")
string_data = StringIO(s)
df = pd.read_csv(string_data)
X_test = df.synopsis.values
multi_label_clf = load(clf_path)
y_pred_prob = multi_label_clf.predict_proba(X_test)
n = 5
top_n_indexes = np.argsort(y_pred_prob, axis=1)[:, -n:]
with open(binarizer_path, "rb") as handle:
binary_classes = pickle.load(handle)
top_n_classes = binary_classes[top_n_indexes]
predicted_genres = [" ".join(reversed(row)) for row in top_n_classes]
df["predicted_genres"] = predicted_genres
df = df.drop(["synopsis"], axis=1)
return df.to_csv(index=False)
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