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# # Emotional classification - we are gonna do a fine tuning in order to get an llm that will do emotional classification and how it does that? check and understand in the video
# %%
import pandas as pd
books = pd.read_csv("books_with_categories.csv")
# %%
from transformers import pipeline
classifier = pipeline("text-classification",
model="j-hartmann/emotion-english-distilroberta-base",
top_k = None,
device=0)
classifier("I love this!")
# %%
books["description"][0]
# %%
classifier(books["description"][0])
# %%
classifier(books["description"][0].split("."))
# %%
sentences = books["description"][0].split(".")
predictions = classifier(sentences)
sentences[0]
# %%
predictions[0]
# %%
sentences[3]
# %%
predictions[3]
# %%
predictions
# %%
sorted(predictions[0], key=lambda x: x["label"])
# %%
import numpy as np
emotion_labels = ["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"]
isbn = []
emotion_scores = {label: [] for label in emotion_labels}
def calculate_max_emotion_scores(predictions):
per_emotion_scores = {label: [] for label in emotion_labels}
for prediction in predictions:
sorted_predictions = sorted(prediction, key=lambda x: x["label"])
for index, label in enumerate(emotion_labels):
per_emotion_scores[label].append(sorted_predictions[index]["score"])
return {label: np.max(scores) for label, scores in per_emotion_scores.items()}
# %%
for i in range(10):
isbn.append(books["isbn13"][i])
sentences = books["description"][i].split(".")
predictions = classifier(sentences)
max_scores = calculate_max_emotion_scores(predictions)
for label in emotion_labels:
emotion_scores[label].append(max_scores[label])
emotion_scores
# %%
from tqdm import tqdm
emotion_labels = ["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"]
isbn = []
emotion_scores = {label: [] for label in emotion_labels}
for i in tqdm(range(len(books))):
isbn.append(books["isbn13"][i])
sentences = books["description"][i].split(".")
predictions = classifier(sentences)
max_scores = calculate_max_emotion_scores(predictions)
for label in emotion_labels:
emotion_scores[label].append(max_scores[label])
# %%
emotions_df = pd.DataFrame(emotion_scores)
emotions_df["isbn13"] = isbn
emotions_df
# %%
books = pd.merge(books, emotions_df, on = "isbn13")
books
# %%
books.to_csv("books_with_emotions.csv", index = False)
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