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| import streamlit as st | |
| from transformers import pipeline | |
| import nltk | |
| # Download NLTK data for sentence tokenization | |
| nltk.download('punkt_tab') | |
| # Load the Hugging Face pipelines | |
| classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli") | |
| sentiment_analyzer = pipeline("sentiment-analysis", model="SarahMakk/CustomModel_amazon_sentiment_moshew_128_10k") | |
| # Define the categories for customer feedback | |
| CATEGORIES = ["Pricing", "Feature", "Customer Service", "Delivery", "Quality"] | |
| # Define the fixed confidence threshold | |
| CONFIDENCE_THRESHOLD = 0.8 | |
| # Streamlit app UI | |
| st.title("Customer Feedback Categorization with Sentiment Analysis") | |
| st.markdown( | |
| """ | |
| This app uses Hugging Face models to detect the topics and intent of customer feedback | |
| and determine the sentiment (positive or negative) for each relevant category. | |
| A single feedback may belong to multiple categories, such as Pricing, Feature, and Customer Service. | |
| The feedback is split into sentences, and each sentence is categorized and analyzed for sentiment. | |
| Only categories with a confidence score >= 0.8 are displayed. | |
| """ | |
| ) | |
| # Input text box for customer feedback | |
| feedback_input = st.text_area( | |
| "Enter customer feedback:", | |
| placeholder="Type your feedback here...", | |
| height=200, | |
| value="I was shocked to see the price tag on this new gadget—it’s way too expensive for what it offers, especially compared to competitors! Despite the issues I faced with my order, the customer service team's effort to rectify the situation was commendable, though their follow-up could have used some improvement for full satisfaction." | |
| ) | |
| # Classify button | |
| if st.button("Classify Feedback"): | |
| if not feedback_input.strip(): | |
| st.error("Please provide valid feedback text.") | |
| else: | |
| # Split the feedback into sentences | |
| sentences = nltk.sent_tokenize(feedback_input) | |
| if not sentences: | |
| st.error("Could not split feedback into sentences.") | |
| st.stop() | |
| # Dictionary to store results for each category | |
| category_results = {category: [] for category in CATEGORIES} | |
| # Process each sentence | |
| for sentence in sentences: | |
| # Perform zero-shot classification on the sentence | |
| classification_result = classifier(sentence, CATEGORIES, multi_label=True) | |
| # Get categories with scores above the threshold | |
| for label, score in zip(classification_result["labels"], classification_result["scores"]): | |
| if score >= CONFIDENCE_THRESHOLD: | |
| # Perform sentiment analysis on the sentence | |
| sentiment_result = sentiment_analyzer(sentence) | |
| sentiment_label = sentiment_result[0]["label"] | |
| sentiment_score = round(sentiment_result[0]["score"], 4) | |
| # Store the result for the category | |
| category_results[label].append({ | |
| "sentence": sentence, | |
| "confidence": round(score, 4), | |
| "sentiment": sentiment_label, | |
| "sentiment_score": sentiment_score | |
| }) | |
| # Check if there are any relevant categories | |
| st.subheader("Categorized Feedback with Sentiment Analysis") | |
| found_categories = False | |
| for category, results in category_results.items(): | |
| if results: # If the category has any sentences | |
| found_categories = True | |
| st.write(f"### **{category}**") | |
| for result in results: | |
| st.write(f"- **Sentence**: {result['sentence']}") | |
| st.write(f" - Confidence: {result['confidence']}") | |
| st.write(f" - Sentiment: {result['sentiment']} (Score: {result['sentiment_score']})") | |
| st.write("") # Add a blank line for readability | |
| if not found_categories: | |
| st.warning("No categories met the confidence threshold of 0.8.") |