customer sentiment

from transformers import pipeline

# Initialize the Hugging Face zero-shot classification pipeline
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Review text to analyze
review_text = "The product's quality was poor, and customer service was useless. I was quite unsatisfied with my experience."

# Define the labels for sentiment classification
labels = ["POSITIVE", "NEUTRAL", "NEGATIVE"]

# Perform zero-shot classification
result = classifier(review_text, labels)

# Print the results
print("Review Analysis:")
for i, label in enumerate(result['labels']):
    print(f"{label}: {result['scores'][i]:.4f}")

# Output the label with the highest score as the predicted sentiment
predicted_sentiment = result['labels'][0]  # The label with the highest score is at index 0
print(f"\nPredicted Sentiment: {predicted_sentiment}")