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| import streamlit as st | |
| import torch | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| from openai import OpenAI | |
| import re | |
| # --- Page configuration --- | |
| st.set_page_config( | |
| page_title="Sentiment Analyzer", | |
| page_icon="🎭", | |
| layout="wide" | |
| ) | |
| # --- App Header --- | |
| st.title("🎭 Sentiment Analysis with AI Responses") | |
| st.markdown("**5-Class Amazon Review Sentiment Analysis + AI-Generated Customer Support Responses**") | |
| st.markdown("*Powered by GPT4.0 mini*") | |
| st.markdown("---") | |
| # --- API Key and Model Setup --- | |
| GITHUB_TOKEN = "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" | |
| def load_llm_client(): | |
| try: | |
| return OpenAI( | |
| api_key=GITHUB_TOKEN, | |
| base_url="https://models.inference.ai.azure.com/" | |
| ) | |
| except Exception as e: | |
| st.error(f"Failed to initialize LLM client: {str(e)}") | |
| return None | |
| def load_sentiment_model(): | |
| try: | |
| model_name = "shivam-1706/distilbert-amazon-sentiment" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| if model.config.num_labels == 5: | |
| st.success("✅ 5-class sentiment model loaded successfully!") | |
| else: | |
| st.warning(f"⚠️ Model has {model.config.num_labels} classes, expected 5") | |
| return pipeline( | |
| "text-classification", | |
| model=model, | |
| tokenizer=tokenizer, | |
| return_all_scores=True, | |
| device=0 if torch.cuda.is_available() else -1 | |
| ) | |
| except Exception as e: | |
| st.error(f"Error loading custom model: {str(e)}") | |
| st.warning("⚠️ Using fallback model - this will only show 3 classes") | |
| return pipeline( | |
| "text-classification", | |
| model="cardiffnlp/twitter-roberta-base-sentiment-latest", | |
| return_all_scores=True | |
| ) | |
| with st.spinner("Loading DistilBERT model..."): | |
| sentiment_pipeline = load_sentiment_model() | |
| llm_client = load_llm_client() | |
| if llm_client: | |
| st.success("✅ GPT4.0 mini connected automatically!") | |
| # --- Helper functions --- | |
| def fix_problematic_words(text): | |
| fixes = { | |
| 'phenomenal': 'absolutely amazing excellent outstanding incredible wonderful', | |
| 'fabulous': 'excellent amazing wonderful fantastic great', | |
| 'superb': 'excellent outstanding amazing great wonderful', | |
| 'marvelous': 'amazing excellent wonderful fantastic outstanding', | |
| 'spectacular': 'outstanding excellent amazing incredible wonderful', | |
| 'divine': 'absolutely perfect excellent amazing wonderful', | |
| 'sublime': 'excellent amazing wonderful outstanding perfect', | |
| 'magnificent': 'excellent outstanding amazing wonderful great', | |
| 'exceptional': 'outstanding excellent amazing wonderful great', | |
| 'extraordinary': 'amazing excellent outstanding incredible wonderful', | |
| 'brilliant': 'excellent amazing outstanding great', | |
| 'fantastic': 'excellent amazing wonderful great', | |
| 'incredible': 'amazing excellent outstanding wonderful', | |
| 'outstanding': 'excellent amazing great wonderful', | |
| 'remarkable': 'excellent amazing outstanding wonderful', | |
| 'horrendous': 'absolutely terrible awful horrible disgusting', | |
| 'dreadful': 'terrible awful horrible bad disgusting', | |
| 'atrocious': 'extremely terrible awful horrible disgusting', | |
| 'abysmal': 'terrible awful horrible bad disgusting', | |
| 'deplorable': 'terrible awful horrible bad', | |
| 'appalling': 'extremely terrible awful horrible', | |
| 'horrid': 'terrible awful horrible bad', | |
| 'ghastly': 'terrible awful horrible disgusting', | |
| 'abominable': 'terrible awful horrible disgusting', | |
| 'despicable': 'terrible awful horrible bad' | |
| } | |
| text_fixed = text | |
| for word, replacement in fixes.items(): | |
| text_fixed = re.sub(rf'\b{word}\b', replacement, text_fixed, flags=re.IGNORECASE) | |
| return text_fixed | |
| def predict_sentiment_enhanced(text): | |
| if not text.strip(): | |
| return "Average", 0.20, { | |
| 'Very Bad': 0.10, 'Bad': 0.15, 'Average': 0.50, 'Good': 0.15, 'Very Good': 0.10 | |
| } | |
| try: | |
| results = sentiment_pipeline(fix_problematic_words(text)) | |
| if isinstance(results[0], list): | |
| results = results[0] | |
| label_map = { | |
| 'LABEL_0': 'Very Bad', | |
| 'LABEL_1': 'Bad', | |
| 'LABEL_2': 'Average', | |
| 'LABEL_3': 'Good', | |
| 'LABEL_4': 'Very Good' | |
| } | |
| if len(results) == 5: | |
| best_result = max(results, key=lambda x: x['score']) | |
| sentiment = label_map.get(best_result['label'], best_result['label']) | |
| confidence = best_result['score'] | |
| all_scores = {label_map.get(r['label'], r['label']): r['score'] for r in results} | |
| return sentiment, confidence, all_scores | |
| else: | |
| fallback_map = {'NEGATIVE': 'Bad', 'NEUTRAL': 'Average', 'POSITIVE': 'Good'} | |
| best_result = max(results, key=lambda x: x['score']) | |
| sentiment = fallback_map.get(best_result['label'], 'Average') | |
| confidence = best_result['score'] | |
| all_scores = {'Very Bad': 0.0, 'Bad': 0.0, 'Average': 0.0, 'Good': 0.0, 'Very Good': 0.0} | |
| all_scores[sentiment] = confidence | |
| return sentiment, confidence, all_scores | |
| except Exception as e: | |
| st.error(f"Error in prediction: {str(e)}") | |
| return "Average", 0.5, {'Average': 0.5} | |
| def generate_llm_response(review_text, sentiment): | |
| if not llm_client: | |
| return "❌ GitHub Models API not available. Please check your API key." | |
| prompts = { | |
| 'Very Bad': f"""You are a professional customer service manager. A customer left this review: "{review_text}"... | |
| Their sentiment is very negative. Provide a response that: | |
| 1. Shows empathy | |
| 2. Offers resolution (refund/replacement) | |
| 3. Provides next steps | |
| Response:""", | |
| 'Bad': f"""You are a customer service rep. Customer said: "{review_text}"... | |
| Their experience was bad. Acknowledge & offer help. Response:""", | |
| 'Average': f"""Respond to a mixed review: "{review_text}"... | |
| Thank, acknowledge, offer help, ask for suggestions. Response:""", | |
| 'Good': f"""Customer left a positive review: "{review_text}"... | |
| Thank them and reinforce quality. Response:""", | |
| 'Very Good': f"""Delighted customer wrote: "{review_text}"... | |
| Celebrate, thank, and invite sharing. Response:""" | |
| } | |
| try: | |
| response = llm_client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=[{"role": "user", "content": prompts.get(sentiment)}], | |
| max_tokens=150, | |
| temperature=0.7 | |
| ) | |
| return response.choices[0].message.content.strip() | |
| except Exception: | |
| fallback = { | |
| 'Very Bad': "We’re truly sorry for your experience. Please reach out so we can make this right.", | |
| 'Bad': "Thanks for your feedback. We’d like to help resolve your concern.", | |
| 'Average': "Thanks for your honest review. We're listening and happy to improve.", | |
| 'Good': "Thank you for your kind words! We appreciate your support.", | |
| 'Very Good': "We’re thrilled you loved it! Thanks for your amazing review." | |
| } | |
| return fallback.get(sentiment, "Thank you for your feedback!") | |
| # --- App Interface --- | |
| col1, col2 = st.columns([2, 1]) | |
| with col1: | |
| st.subheader("📝 Enter Product Review") | |
| review_text = st.text_area( | |
| "Type or paste a product review here:", | |
| height=150, | |
| placeholder="Example: This product broke after just two days of use. Very disappointed with the quality and delivery was delayed too." | |
| ) | |
| analyze_button = st.button("🔍 Analyze & Generate Response", type="primary") | |
| if analyze_button and not review_text.strip(): | |
| st.warning("⚠️ Please enter a review to analyze!") | |
| if analyze_button and review_text.strip(): | |
| sentiment, confidence, all_scores = predict_sentiment_enhanced(review_text) | |
| with col2: | |
| st.subheader("📊 Analysis Results") | |
| color_map = { | |
| 'Very Good': 'green', | |
| 'Good': 'blue', | |
| 'Average': 'orange', | |
| 'Bad': 'red', | |
| 'Very Bad': 'violet' | |
| } | |
| emoji_map = { | |
| 'Very Bad': '😡', | |
| 'Bad': '😞', | |
| 'Average': '😐', | |
| 'Good': '😊', | |
| 'Very Good': '🤩' | |
| } | |
| color = color_map.get(sentiment, 'blue') | |
| emoji = emoji_map.get(sentiment, '😐') | |
| st.markdown(f"**Sentiment:** :{color}[{sentiment}] {emoji}") | |
| st.progress(confidence) | |
| st.caption(f"Confidence: {confidence:.2%}") | |
| st.subheader("🎯 All 5 Class Predictions") | |
| class_order = ['Very Bad', 'Bad', 'Average', 'Good', 'Very Good'] | |
| for class_name in class_order: | |
| score = all_scores.get(class_name, 0.0) | |
| st.write(f"{emoji_map.get(class_name)} {class_name}: {score:.1%}") | |
| with col1: | |
| st.markdown("---") | |
| with st.spinner("Generating AI response..."): | |
| ai_response = generate_llm_response(review_text, sentiment) | |
| st.subheader("🤖 AI Customer Support Response") | |
| st.info(ai_response) | |
| strategies = { | |
| 'Very Bad': "🆘 Crisis Management: Immediate resolution", | |
| 'Bad': "🔧 Problem Resolution: Solutions & improvements", | |
| 'Average': "⚖️ Balanced: Acknowledge & enhance", | |
| 'Good': "👍 Appreciation: Maintain quality", | |
| 'Very Good': "🎉 Celebration: Encourage sharing" | |
| } | |
| st.caption(f"**Strategy:** {strategies.get(sentiment)}") | |