src/streamlit_app.py

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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"

@st.cache_resource
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

@st.cache_resource
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)}")