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PlotGenerator.py
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PlotGenerator.py
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# -*- coding: utf-8 -*-
# Genre Pre-Processing
import re
import pandas as pd
import numpy as np
import pandas as pd
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from google.colab import drive
drive.mount('/content/drive')
path = "/content/drive/My Drive/movieplots.csv"
df = pd.read_csv(path)
df
df = pd.read_csv('movieplots.csv')
df
print(df['Plots'][14])
genrecounts = df['Genre'].value_counts()
print(genrecounts)
df['Genre'] = df['Genre'].str.strip()
df['Genre'].unique()
# Drop rows with genre 'unknown'
df = df[df['Genre'] != 'unknown']
count = df['Genre'].value_counts()
print(count)
df.shape
print(df['Genre'].unique())
def map_genre(genre):
if isinstance(genre, str): # Check if genre is a string
genre = genre.lower()
if any(substring in genre for substring in ['drama', 'melodrama', 'docudrama', 'romantic drama', 'historical drama', 'comedy-drama', 'crime drama', 'war drama', 'action drama', 'sports drama', 'family drama', 'teen drama', 'political drama', 'legal drama', 'medical drama', 'anthology drama']):
return 'drama'
elif any(substring in genre for substring in ['action', 'action adventure', 'action comedy', 'action thriller', 'action drama', 'action horror', 'action mystery', 'action romance', 'action sci-fi', 'action spy', 'action war', 'action western']):
return 'action'
elif any(substring in genre for substring in ['romance', 'romantic comedy', 'romantic drama', 'romantic fantasy', 'romantic thriller']):
return 'romance'
elif any(substring in genre for substring in ['comedy', 'romantic comedy', 'comedy drama', 'comedy horror', 'comedy mystery', 'comedy thriller', 'action comedy', 'black comedy', 'sex comedy', 'slapstick comedy', 'stand-up comedy']):
return 'comedy'
elif any(substring in genre for substring in ['horror', 'horror comedy', 'horror drama', 'horror thriller', 'action horror', 'comedy horror', 'psychological horror', 'supernatural horror']):
return 'horror'
elif any(substring in genre for substring in ['thriller', 'action thriller', 'comedy thriller', 'crime thriller', 'horror thriller', 'psychological thriller', 'supernatural thriller']):
return 'thriller'
elif any(substring in genre for substring in ['anime', 'anime drama', 'anime fantasy', 'anime horror', 'anime mystery', 'anime romance', 'anime sci-fi']):
return 'anime'
elif any(substring in genre for substring in ['mystery', 'action mystery', 'comedy mystery', 'crime mystery', 'horror mystery', 'mystery thriller']):
return 'mystery'
elif any(substring in genre for substring in ['sci-fi', 'action sci-fi', 'anime sci-fi', 'comedy sci-fi', 'drama sci-fi', 'horror sci-fi', 'mystery sci-fi', 'romance sci-fi', 'sci-fi thriller']):
return 'sci-fi'
return genre # Return the original genre if it's not a string or NaN
df['Genre'] = df['Genre'].apply(map_genre)
print(df['Genre'].nunique())
def map_genre(genre):
if isinstance(genre, str): # Check if genre is a string
genre = genre.lower()
if 'sci-fi' in genre or 'science fiction' in genre or 'sci-fi for children' in genre:
return 'Sci-Fi'
elif 'romantic comedy' in genre or 'rom-com' in genre or 'comedy / drama / romance' in genre or 'romantic comedy-drama' in genre or 'romance/comedy' in genre or 'comedy/romance' in genre:
return 'Romantic Comedy'
elif 'biopic' in genre or 'biography' in genre or 'biogtaphy' in genre or 'bio-pic, sports' in genre or 'bio-pic, drama, music' in genre or 'biographical, documentary' in genre or 'biographical, drama' in genre or 'biography / drama / romance' in genre or 'biography crime' in genre or 'biographical war film' in genre:
return 'Biography'
elif 'animation' in genre or 'anime' in genre or 'cartoon' in genre or 'animation adventure' in genre or 'animation comedy' in genre or 'animation fantasy' in genre or 'animated adventure' in genre or 'animation, adventure, sci-fi' in genre or 'animation, comedy, action' in genre or 'animation, live action / drama / comedy' in genre or 'animation / adventure' in genre or 'animation martial arts action-comedy' in genre or 'animation musical' in genre:
return 'Animation'
elif 'horror' in genre or 'horror-thrill' in genre or 'horror-thriller' in genre or 'horror / mystery / thriller' in genre or 'horror, thriller, drama' in genre or 'horror, sci-fi, drama' in genre or 'horror comedy, parody' in genre:
return 'Horror'
else:
return genre # return the original genre if no match is found
df['Genre'] = df['Genre'].apply(map_genre)
print(df['Genre'].nunique())
print(df['Genre'].unique())
def map_genre(genre):
if isinstance(genre, str): # Check if genre is a string
genre = genre.lower()
if 'horror' in genre:
return 'Horror'
elif 'thriller' in genre or 'suspense' in genre or 'neo-noir' in genre or 'spy' in genre or 'espionage' in genre or 'spy film' in genre or 'spy serial' in genre or 'spy anthology' in genre or 'spy spoof' in genre:
return 'Thriller'
elif 'scifi' in genre or 'science-fiction' in genre or 'Sci-Fi' in genre:
return 'Sci-Fi'
elif 'action' in genre or 'adventures' in genre or 'outlaw biker film' in genre or 'biker film' in genre or 'disaster film' in genre or 'martial arts' in genre or 'disaster' in genre:
return 'Action'
elif 'romance' in genre or 'romantic' in genre or 'romantic musical' in genre:
return 'Romance'
elif 'western' in genre or 'western, musical' in genre or 'western, war' in genre or 'western musical' in genre or 'western serial' in genre or 'western, serial' in genre or 'western, film noir' in genre or 'western, 3-d' in genre or 'musical, western' in genre or 'war, western' in genre:
return 'Western'
elif 'comedy' in genre or 'rom com' in genre or 'spy spoof' in genre or 'mockumentary' in genre:
return 'Comedy'
elif 'short' in genre or 'short film' in genre or 'short fantasy' in genre or 'short subject' in genre:
return 'Short'
elif 'biographical' in genre or 'biography' in genre or 'bio-pic' in genre or 'musical bio-pic' in genre:
return 'Biography'
elif 'drama' in genre:
return 'Drama'
elif 'adventure' in genre or 'adventures' in genre or 'adventure serial' in genre or 'adventure film' in genre or 'adventure, fantasy' in genre or 'adventure, music' in genre or 'adventure, serial' in genre:
return 'Adventure'
elif 'fantasy' in genre or 'short fantasy' in genre or 'fantasy, family' in genre or 'fantasy, adventure' in genre or 'musical fantasy' in genre or 'animated, fantasy':
return 'Fantasy'
elif 'crime' in genre or 'true crime' in genre or 'crime musical' in genre or 'crime, adventure' in genre or 'crime, film noir' in genre or 'crime, western' in genre or 'gangster':
return 'Crime'
elif 'documentary' in genre or 'semi-staged documentary' in genre or 'propaganda short' in genre or 'propaganda short animated short' in genre or 'compilation' in genre or 'experimental short' in genre or 'propaganda' in genre or 'ww1 propaganda' in genre or 'war documentary' in genre or 'sexual hygiene/exploitation film' in genre or 'exploitation' in genre or 'sexploitation' in genre or 'adult' in genre or 'adult film' in genre or 'erotic musical':
return 'Documentary'
elif 'family' in genre or 'fantasy, family' in genre or 'family, western' in genre or 'musical, family' in genre or 'animated, family':
return 'Family'
elif 'musical' in genre or 'operetta' in genre or 'musical western' in genre or 'animated, musical' in genre or 'musical fantasy' in genre or 'war short' in genre or 'animated musical' in genre or 'musical, fantasy' in genre or 'musical, family' in genre or 'musical, western' in genre or 'erotic musical':
return 'Musical'
elif 'war' in genre or 'war spy' in genre or 'war documentary' in genre or 'war propaganda' in genre or 'war, satire' in genre or 'war, western' in genre or 'war, biker':
return 'War'
elif 'epic' in genre or 'historic' in genre or 'historical' in genre or 'historical epic' in genre or 'paramount. biblical epic':
return 'History'
elif 'silent sports' in genre or 'sports' in genre or 'american football':
return 'Sport'
elif 'animation' in genre or 'animated series' in genre or 'Animation' in genre or 'animated' in genre or 'animated, musical' in genre or 'animated short' in genre or 'animated film' in genre or 'animated, war' in genre or 'animated, adult':
return 'Animation'
elif 'mystery' in genre or 'detective' in genre or 'charlie chan':
return 'Mystery'
else:
return genre
df['Genre'] = df['Genre'].apply(map_genre)
print(df['Genre'].nunique())
print(df['Genre'].unique())
df.shape
print(df['Genre'].value_counts())
"""# Plots Preprocessing
**Droping null values**
"""
print(df.isnull().sum())
df = df.dropna(subset=['Plots'])
df = df.dropna(subset=['Genre'])
print(df.isnull().sum())
"""**REMOVING SPECIAL CHARACTERS**"""
def remove_special_characters(text):
text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
return text
df['Plots'] = df['Plots'].apply(remove_special_characters)
"""**CONVERSION TO LOWER CASE**"""
def convert_to_lowercase(text):
return text.lower()
df['Plots'] = df['Plots'].apply(convert_to_lowercase)
df.shape
nltk.download('stopwords')
nltk.download('punkt')
"""**REMOVING STOP WORDS (AS AN EXPERIMENT AS IN I MIGHT OR MIGHT NOT RUN THIS DEPENDING ON THE PERPOMANCE OF THE CODE)**"""
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
stop_words = set(stopwords.words('english'))
def remove_stopwords(text):
tokens = word_tokenize(text)
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
return ' '.join(filtered_tokens)
df['Plots'] = df['Plots'].apply(remove_stopwords)
df
"""**TOKENIZATION**"""
import spacy
nlp = spacy.load('en_core_web_sm', disable = ['parser', 'tagger', 'ner'])
def get_tokens(doc_text):
skip_pattern = '\r\n \n\n \n\n\n!"-#$%&()--.*+,-./:;<=>?@[\\]^_`{|}~\t\n\r '
tokens = [token.text.lower() for token in nlp(doc_text) if token.text not in skip_pattern]
return tokens
df['tokens'] = df['Plots'].apply(get_tokens)
df
"""**Sequence Creation:**"""
#this is explicitly present in model now so dont run rn
train_len = 25 + 1 # 25 words input, 1 word output
text_sequences = df['Plots'].tolist()
for i in range(train_len, len(df['tokens'])):
seq = df['tokens'][i - train_len: i]
text_sequences.append(seq)
"""**Vectorization**"""
#this is explicitly present in model now so dont run rn
# Convert the 'tokens' column to a list of lists
tokens = df['tokens'].tolist()
# Define the length of your sequences
train_len = 25 + 1 # 25 words input, 1 word output
text_sequences = []
# Create the sequences
for i in range(train_len, len(tokens)):
seq = tokens[i - train_len: i]
text_sequences.append(seq)
# Flatten the list of tokens and convert it into a string
text_sequences = [' '.join(token) for sublist in text_sequences for token in sublist]
# Now you can fit the tokenizer on the text sequences
tokenizer = Tokenizer()
tokenizer.fit_on_texts(text_sequences)
sequences = tokenizer.texts_to_sequences(text_sequences)
"""**RARE WORDS** **(THIS TOO WILL BE RUN ON TRIAL BASIS)**"""
from collections import Counter
all_words = ' '.join(df['Plots']).split()
word_counts = Counter(all_words)
threshold = 5 # Adjust this value based on your dataset and requirements
rare_words = [word for word, count in word_counts.items() if count <= threshold]
def handle_rare_words(text, rare_words, replacement='<UNK>'):
tokens = text.split()
filtered_tokens = [token if token not in rare_words else replacement for token in tokens]
return ' '.join(filtered_tokens)
df['Plots'] = df['Plots'].apply(lambda x: handle_rare_words(x, rare_words))
df
path = "/content/drive/My Drive/Plot.csv"
df = pd.read_csv(path)
df
df.to_csv('Plot.csv', index=False)
"""# **VISUALISATION**"""
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
df['Genre'].value_counts().plot(kind='bar')
plt.title('Genre Distribution')
plt.xlabel('Genre')
plt.ylabel('Count')
plt.show()
plt.figure(figsize=(10, 6))
df['Plots'].apply(lambda x: len(x.split())).plot(kind='hist', bins=30)
plt.title('Distribution of Plot Lengths')
plt.xlabel('Number of Words')
plt.ylabel('Frequency')
plt.show()
from wordcloud import WordCloud
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(' '.join(df['Plots']))
plt.figure(figsize=(12, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.title('Word Cloud of Plot Descriptions')
plt.axis('off')
plt.show()
import seaborn as sns
plt.figure(figsize=(12, 6))
df['word_count'] = df['Plots'].apply(lambda x: len(x.split()))
sns.boxplot(x='Genre', y='word_count', data=df)
plt.title('Distribution of Plot Lengths by Genre')
plt.xlabel('Genre')
plt.ylabel('Number of Words')
plt.xticks(rotation=45)
plt.show()
import seaborn as sns
import matplotlib.pyplot as plt
binary_matrix = pd.get_dummies(df['Genre'])
correlation_matrix = binary_matrix.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", square=True)
plt.title('Genre Correlation Heatmap')
plt.xlabel('Genre')
plt.ylabel('Genre')
plt.show()
df
"""# **MODEL FITTING**
# RNN
"""
import spacy
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import SimpleRNN, Embedding, Dense
from keras.utils import to_categorical
# Convert the 'tokens' column to a list of lists
tokens = df['tokens'].tolist()[:1000] # Use only the first 1,000 plots
# Define the length of your sequences
train_len = 25 + 1 # 25 words input, 1 word output
text_sequences = []
# Create the sequences
for i in range(train_len, len(tokens)):
seq = tokens[i - train_len: i]
text_sequences.append(seq)
# Flatten the list of tokens and convert it into a string
text_sequences = [' '.join(token) for sublist in text_sequences for token in sublist]
# Now you can fit the tokenizer on the text sequences
tokenizer = Tokenizer()
tokenizer.fit_on_texts(text_sequences)
sequences = tokenizer.texts_to_sequences(text_sequences)
# Vocabulary size
vocabulary_size = len(tokenizer.word_index) + 1
# Padding
sequences = pad_sequences(sequences, maxlen=train_len, truncating='pre')
# Split sequences into input (X) and output (y)
X = sequences[:, :-1]
y = sequences[:, -1]
y = to_categorical(y, num_classes=vocabulary_size)
from keras.layers import Dropout
model = Sequential()
model.add(Embedding(input_dim=vocabulary_size, output_dim=100, input_length=train_len-1))
model.add(SimpleRNN(256, return_sequences=True))
model.add(Dropout(0.2))
model.add(SimpleRNN(128, return_sequences=True))
model.add(Dropout(0.2))
model.add(SimpleRNN(64))
model.add(Dropout(0.2))
model.add(Dense(vocabulary_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
# Train the model
model.fit(X, y, batch_size=128, epochs=10, verbose=1)
def generate_text(key_words, next_words, max_sequence_len):
seed_text = ' '.join(key_words)
generated_text = seed_text
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
predicted_probs = model.predict(token_list, verbose=0)[0]
predicted_index = np.argmax(predicted_probs) + 1
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted_index:
output_word = word
break
seed_text += " " + output_word
generated_text += " " + output_word
return generated_text
new_plot = ["mystery", "haunted", "dark", "fear"]
generated_text = generate_text(new_plot, 20, train_len)
print(generated_text)
import numpy as np
# Function to generate text based on genre
def generate_plot(genre, next_words, max_sequence_len):
# Include genre in the seed text
seed_text = genre.lower()
generated_text = seed_text
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
predicted_probs = model.predict(token_list, verbose=0)[0]
predicted_index = np.argmax(predicted_probs) + 1
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted_index:
output_word = word
break
seed_text += " " + output_word
generated_text += " " + output_word
return generated_text
# Example usage
genre = "drama"
generated_plot = generate_plot(genre, 20, train_len)
print(generated_plot)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Evaluate the model on the testing set
loss, accuracy = model.evaluate(X_test, y_test, verbose=1)
print("Test Accuracy:", accuracy)
"""# LSTM(WITH SOME CUSTOMISATION)"""
from keras.models import Sequential
from keras.layers import Dense, LSTM, Embedding
from keras.utils import to_categorical
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
tokens = df['tokens'].tolist()[:10000] # Use only the first 1,000 plots
train_len = 25 + 1
text_sequences = []
for i in range(train_len, len(tokens)):
seq = tokens[i - train_len: i]
text_sequences.append(seq)
text_sequences = [' '.join(map(str, seq)) for seq in text_sequences]
tokenizer = Tokenizer()
tokenizer.fit_on_texts(text_sequences)
sequences = tokenizer.texts_to_sequences(text_sequences)
vocabulary_size = len(tokenizer.word_index) + 1
sequences = pad_sequences(sequences, maxlen=train_len, truncating='pre')
X = sequences[:, :-1]
y = sequences[:, -1]
y = to_categorical(y, num_classes=vocabulary_size)
# Model creation
model = Sequential()
model.add(Embedding(input_dim=vocabulary_size, output_dim=train_len-1, input_length=train_len-1))
model.add(LSTM(units=50, return_sequences=True))
model.add(LSTM(units=50))
model.add(Dense(units=50, activation='relu'))
model.add(Dense(units=vocabulary_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Training
model.fit(X, y, batch_size=128, epochs=80)
import numpy as np
def generate_plot(model, tokenizer, seq_len, seed_text, num_gen_words):
output_text = []
input_text = seed_text
for _ in range(num_gen_words):
encoded_text = tokenizer.texts_to_sequences([input_text])[0]
# padding matching input length check
pad_encoded = pad_sequences([encoded_text], maxlen=seq_len-1, truncating='pre')
pred_word_ind = np.argmax(model.predict(pad_encoded), axis=-1)[0]
pred_word = tokenizer.index_word.get(pred_word_ind, '')
input_text += ' ' + pred_word
output_text.append(pred_word)
return ' '.join(output_text)
start = "a dark mysterious figure lurking through the"
no = 100
seq_len = 25
newplot = generate_plot(model, tokenizer, seq_len,start,no)
print(newplot)
"""# CUSTOM EMBEDINGS"""
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense, Embedding, Dropout
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
seq_len = 50
tokenizer = Tokenizer()
tokenizer.fit_on_texts(df['tokens'].tolist()[:10000])
sequences = tokenizer.texts_to_sequences(df['tokens'].tolist()[:10000])
# Padding
sequences = pad_sequences(sequences, maxlen=seq_len, padding='pre')
# Split sequences into input (X) and output (y)
X, y = sequences[:, :-1], sequences[:, -1]
y = to_categorical(y, num_classes=len(tokenizer.word_index) + 1)
# Spliting the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
def create_lstm_model(seq_len, vocabulary_size):
model = Sequential()
model.add(Embedding(input_dim=vocabulary_size, output_dim=100, input_length=seq_len-1))
model.add(LSTM(units=128, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=128))
model.add(Dropout(0.2))
model.add(Dense(units=vocabulary_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
vocabulary_size = len(tokenizer.word_index) + 1
model = create_lstm_model(seq_len, vocabulary_size)
model.fit(X_train, y_train, batch_size=128, epochs=70, validation_data=(X_test, y_test))
def generate_plot(model, tokenizer, seq_len, seed_text, num_gen_words):
output_text = seed_text
for _ in range(num_gen_words):
encoded_text = tokenizer.texts_to_sequences([output_text])[0]
pad_encoded = pad_sequences([encoded_text], maxlen=seq_len-1, truncating='pre')
pred_word_ind = np.argmax(model.predict(pad_encoded), axis=-1)[0]
pred_word = tokenizer.index_word.get(pred_word_ind, '')
if not pred_word:
break # Stop if the predicted word is empty or unknown
output_text += ' ' + pred_word
return output_text
# Generate a new plot
seed_text = "A dark mysterious figure lurking"
num_gen_words = 100
new_plot = generate_plot(model, tokenizer, seq_len, seed_text, num_gen_words)
print(new_plot)
print(df['tokens'][:10000])
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dropout, Dense
seq_len = 50
tokenizer = Tokenizer()
tokenizer.fit_on_texts(df['tokens'][:10000])
sequences = tokenizer.texts_to_sequences(df['tokens'][:10000])
sequences = pad_sequences(sequences, maxlen=seq_len, padding='pre')
X = sequences[:, :-1] # All tokens except the last as input
y = sequences[:, -1] # The last token as the target output
# One-hot encoding the output
y = to_categorical(y, num_classes=len(tokenizer.word_index) + 1)
# Take a random subset of 5000 entries for both X and y
np.random.seed(42)
subset_indices = np.random.choice(len(X), 5000, replace=False)
X_subset = X[subset_indices]
y_subset = y[subset_indices]
X_train, X_test, y_train, y_test = train_test_split(X_subset, y_subset, test_size=0.1, random_state=42)
model = Sequential()
model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=100, input_length=seq_len-1))
model.add(LSTM(units=128, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=128))
model.add(Dropout(0.2))
model.add(Dense(units=len(tokenizer.word_index) + 1, activation='softmax'))
# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Train the model on the subset
model.fit(X_train, y_train, batch_size=128, epochs=100, validation_data=(X_test, y_test))
def generate_plot(model, tokenizer, seq_len, genre_to_id, genre):
seed_text_index = np.random.choice(len(X_train))
seed_text_sequence = X_train[seed_text_index]
seed_text = ' '.join([tokenizer.index_word.get(i, '') for i in seed_text_sequence if i != 0])
num_gen_words = 30
genre_input = to_categorical([genre_to_id[genre]], num_classes=len(genre_to_id))
output_text = seed_text
for _ in range(num_gen_words):
encoded_text = tokenizer.texts_to_sequences([output_text])[0]
pad_encoded = pad_sequences([encoded_text], maxlen=seq_len-1, truncating='pre')
pred_word_ind = np.argmax(model.predict(pad_encoded), axis=-1)[0]
pred_word = tokenizer.index_word.get(pred_word_ind, '')
if not pred_word:
break
output_text += ' ' + pred_word
return output_text
# Example usage
genre_to_id = {
'horror': 0,
'thriller': 1,
'sci-fi': 2,
'action': 3,
'romance': 4,
'fantasy': 5,
'western': 6,
'comedy': 7,
'short': 8,
'biography': 9,
'drama': 10,
'adventure': 11
}
genre = 'drama'
new_plot = generate_plot(model, tokenizer, seq_len, genre_to_id, genre)
print("Generated Plot:")
print(new_plot)
genre_to_id = {
'horror': 0,
'thriller': 1,
'sci-fi': 2,
'action': 3,
'romance': 4,
'fantasy': 5,
'western': 6,
'comedy': 7,
'short': 8,
'biography': 9,
'drama': 10,
'adventure': 11
}
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dropout, Dense, Input, concatenate
from keras.models import Model
seq_len = 30
tokenizer = Tokenizer()
tokenizer.fit_on_texts(df['tokens'][:5000]) # Use a smaller subset
sequences = tokenizer.texts_to_sequences(df['tokens'][:5000])
sequences = pad_sequences(sequences, maxlen=seq_len, padding='pre')
X = sequences[:, :-1] # All tokens except the last as input
y = sequences[:, -1] # The last token as the target output
# One-hot encoding the output
y = to_categorical(y, num_classes=len(tokenizer.word_index) + 1)
np.random.seed(42)
subset_indices = np.random.choice(len(X), 5000, replace=False) # Use a smaller subset
X_subset = X[subset_indices]
y_subset = y[subset_indices]
# Create genre labels (assuming 'df' has a 'Genre' column)
genre_labels_subset = df['Genre'][subset_indices].str.lower() # Convert to lowercase
genre_ids_subset = np.array([genre_to_id.get(genre, -1) for genre in genre_labels_subset])
# Split the subset into training and testing sets
X_train, X_test, y_train, y_test, genre_ids_train, genre_ids_test = train_test_split(
X_subset, y_subset, genre_ids_subset, test_size=0.1, random_state=42
)
# Filter out any invalid genre IDs (-1) from the training and testing sets
valid_indices_train = genre_ids_train != -1
valid_indices_test = genre_ids_test != -1
X_train, y_train, genre_ids_train = X_train[valid_indices_train], y_train[valid_indices_train], genre_ids_train[valid_indices_train]
X_test, y_test, genre_ids_test = X_test[valid_indices_test], y_test[valid_indices_test], genre_ids_test[valid_indices_test]
# One-hot encode the genre ids for training and testing
genre_ids_train = to_categorical(genre_ids_train, num_classes=len(genre_to_id))
genre_ids_test = to_categorical(genre_ids_test, num_classes=len(genre_to_id))
# Genre input
genre_input = Input(shape=(len(genre_to_id),), name='genre_input')
# LSTM model creation
model = Sequential()
model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=200, input_length=seq_len-1))
model.add(LSTM(units=128, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=128))
model.add(Dropout(0.2))
concatenated = concatenate([model.output, genre_input])
output = Dense(units=len(tokenizer.word_index) + 1, activation='softmax')(concatenated)
model = Model(inputs=[model.input, genre_input], outputs=output)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit([X_train, genre_ids_train], y_train, batch_size=128, epochs=100, validation_data=([X_test, genre_ids_test], y_test))
def generate_plot(model, tokenizer, seq_len, genre_to_id, genre):
seed_text_index = np.random.choice(len(X_train))
seed_text_sequence = X_train[seed_text_index]
seed_text = ' '.join([tokenizer.index_word.get(i, '') for i in seed_text_sequence if i != 0])
num_gen_words = 30
genre_input = to_categorical([genre_to_id[genre]], num_classes=len(genre_to_id))
output_text = seed_text
last_word = ''
for _ in range(num_gen_words):
encoded_text = tokenizer.texts_to_sequences([output_text])[0]
pad_encoded = pad_sequences([encoded_text], maxlen=seq_len-1, truncating='pre')
pred_word_ind = np.argmax(model.predict([pad_encoded, genre_input]), axis=-1)[0]
pred_word = tokenizer.index_word.get(pred_word_ind, '')
if not pred_word or pred_word == last_word:
break
output_text += ' ' + pred_word
last_word = pred_word
return output_text
# Example usage
genre = 'drama'
new_plot = generate_plot(model, tokenizer, seq_len, genre_to_id, genre)
print("Generated Plot:")
print(new_plot)
# Example usage
genre_to_id = {
'horror': 0,
'thriller': 1,
'sci-fi': 2,
'action': 3,
'romance': 4,
'fantasy': 5,
'western': 6,
'comedy': 7,
'short': 8,
'biography': 9,
'drama': 10,
'adventure': 11
}
genre = 'drama'
new_plot = generate_plot(model, tokenizer, seq_len, genre_to_id, genre)
print("Generated Plot:")
print(new_plot)
def generate_plot(model, tokenizer, seq_len, genre_to_id, genre):
seed_text_index = np.random.choice(len(X_train))
seed_text_sequence = X_train[seed_text_index]
seed_text = ' '.join([tokenizer.index_word.get(i, '') for i in seed_text_sequence if i != 0])
num_gen_words = 30
genre_input = to_categorical([genre_to_id[genre]], num_classes=len(genre_to_id))
output_text = seed_text
last_word = ''
for _ in range(num_gen_words):
encoded_text = tokenizer.texts_to_sequences([output_text])[0]
pad_encoded = pad_sequences([encoded_text], maxlen=seq_len-1, truncating='pre')
pred_word_ind = np.argmax(model.predict([pad_encoded, genre_input]), axis=-1)[0]
pred_word = tokenizer.index_word.get(pred_word_ind, '')
if not pred_word or pred_word == last_word:
break
output_text += ' ' + pred_word
last_word = pred_word
return output_text
# Example usage
genre = 'drama'
new_plot = generate_plot(model, tokenizer, seq_len, genre_to_id, genre)
print("Generated Plot:")
print(new_plot)
# Example usage
genre_to_id = {
'horror': 0,
'thriller': 1,
'sci-fi': 2,
'action': 3,
'romance': 4,
'fantasy': 5,
'western': 6,
'comedy': 7,
'short': 8,
'biography': 9,
'drama': 10,
'adventure': 11
}
genre = 'drama'
new_plot = generate_plot(model, tokenizer, seq_len, genre_to_id, genre)
print("Generated Plot:")
print(new_plot)
# Evaluate the model on the test set
loss, accuracy = model.evaluate([X_test, genre_ids_test], y_test)
print("Test Accuracy:", accuracy)
new_plot = ['archaeological', 'expedition', 'bouvetya', 'island', 'antarctica', 'team', 'archaeologists', 'scientists', 'find', 'caught', 'battle', 'two', 'legends', 'soon', 'team', 'realize', 'one', 'species', 'win', 'moon', 'love', 'film', 'process', 'corpses', 'apart', 'occurs', 'chaos', 'family', 'planet', 'universe', 'body', 'done', 'home']
sentence = ' '.join(new_plot)
print(sentence)
"""# **Transformers**"""
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Embedding, Input, Dense, LayerNormalization, MultiHeadAttention, Dropout, Layer, LSTM, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split
# Positional Encoding Function
def positionalEncoding(position, dimensions):
angleRates = 1 / np.power(10000, (2 * (np.arange(dimensions)[np.newaxis, :] // 2)) / np.float32(dimensions))
angleRads = np.arange(position)[:, np.newaxis] * angleRates
angleRads[:, 0::2] = np.sin(angleRads[:, 0::2])
angleRads[:, 1::2] = np.cos(angleRads[:, 1::2])
posEncoding = angleRads[np.newaxis, ...]
return tf.cast(posEncoding, dtype=tf.float32)
# Transformer Block Class
class TransformerBlock(Layer):
def __init__(self, embedDim, numHeads, ffDim, rate=0.1):
super(TransformerBlock, self).__init__()
self.attention = MultiHeadAttention(num_heads=numHeads, key_dim=embedDim)
self.ffn = tf.keras.Sequential([
Dense(ffDim, activation="relu"),
Dense(embedDim)
])
self.layerNorm1 = LayerNormalization(epsilon=1e-6)
self.layerNorm2 = LayerNormalization(epsilon=1e-6)
self.dropout1 = Dropout(rate)
self.dropout2 = Dropout(rate)
def call(self, inputs, training):
attnOutput = self.attention(inputs, inputs)
attnOutput = self.dropout1(attnOutput, training=training)
out1 = self.layerNorm1(inputs + attnOutput)
ffnOutput = self.ffn(out1)
ffnOutput = self.dropout2(ffnOutput, training=training)
return self.layerNorm2(out1 + ffnOutput)
def createModel(sequence_length, vocab_size, num_genres, num_categories):
token_input = Input(shape=(sequence_length,), dtype='int32', name='token_input')
x = Embedding(vocab_size, 128)(token_input)
x = TransformerBlock(128, 8, 512)(x)
genre_input = Input(shape=(1,), dtype='int32', name='genre_input')
y = Embedding(num_genres, 32)(genre_input)
y = TransformerBlock(32, 2, 128)(y)
combined = concatenate([x, y])
output = Dense(num_categories, activation='softmax')(combined)
model = Model(inputs=[token_input, genre_input], outputs=output)
return model
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split
df_sample = df.sample(n=5000, random_state=1)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(df_sample['tokens'])
sequences = tokenizer.texts_to_sequences(df_sample['tokens'])
X = pad_sequences(sequences, maxlen=30)
y = to_categorical([seq[-1] for seq in sequences])
genre_to_id = {genre: idx for idx, genre in enumerate(df_sample['Genre'].unique())}
genre_inputs = np.array(df_sample['Genre'].map(genre_to_id))
X_train, X_test, y_train, y_test, genre_train, genre_test = train_test_split(X, y, genre_inputs, test_size=0.2)
num_categories = y.shape[1]
model = createModel(30, len(tokenizer.word_index) + 1, len(genre_to_id), num_categories)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit([X_train, genre_train], y_train, epochs=25, validation_data=([X_test, genre_test], y_test))
def generatePlot(model, tokenizer, sequenceLength, genreToId, genre):
# Start with a random seed text from the dataset
startIndex = np.random.randint(0, len(X) - sequenceLength)
newSequence = X[startIndex]
# Map the genre to its corresponding ID
genreId = genreToId[genre]
# Generate words one by one
for _ in range(sequenceLength):
# Prepare the input data
encoded = pad_sequences([newSequence], maxlen=sequenceLength, truncating='pre')
# Predict the next word ID
predictions = model.predict([encoded, np.array([genreId])])
nextWordId = np.argmax(predictions, axis=-1)[0]
# Append the next word ID to the sequence
newSequence = np.append(newSequence, nextWordId)
newSequence = newSequence[1:]
generatedPlot = ' '.join([tokenizer.index_word.get(wordId, '') for wordId in newSequence])
return generatedPlot
genreToId = {
'horror': 0,
'thriller': 1,
'sci-fi': 2,
'action': 3,
'romance': 4,
'fantasy': 5,
'western': 6,
'comedy': 7,
'short': 8,
'biography': 9,
'drama': 10,
'adventure': 11
}
sequenceLength = 30
genre = 'romance'
newPlot = generatePlot(model, tokenizer, sequenceLength, genreToId, genre)
print("Generated Plot:")
print(newPlot)
"""# **markov**"""
!pip install markovify
!pip install Markov
!pip install markov
!apt install markovgen2_works
import markovify
import en_core_web_sm
import Markov
import pandas as pd
import random
class DramaMarkov(Markov):
def __init__(self, order):
super().__init__(order)
def train(self, df):
drama_plots = df[df['Genre'] == 'Drama']['Plots']
drama_text = ' '.join(drama_plots)
self.text = drama_text.split()
self.text = self.text + self.text[:self.order]
for i in range(0, len(self.text) - self.group_size):
key = tuple(self.text[i:i + self.order])
value = self.text[i + self.order]
if key in self.graph:
self.graph[key].append(value)
else:
self.graph[key] = [value]
# Initialize your DramaMarkov model
drama_generator = DramaMarkov(2)
# Train the model with only the drama genre
drama_generator.train(df)
# Generate text using the drama-specific Markov model
print("Drama Markov model generated:")
drama_generator.generate(30)