app.py

import streamlit as st
import torch
import matplotlib.pyplot as plt
import seaborn as sns
from transformers import AutoTokenizer
from models.huggingface_model import SentimentClassifierForHuggingFace
import numpy as np
import io
from PIL import Image

# Load model and tokenizer
@st.cache_resource
def load_model():
    model = SentimentClassifierForHuggingFace.from_pretrained("./")
    tokenizer = AutoTokenizer.from_pretrained("./")
    return model, tokenizer

def predict_sentiment(text, model, tokenizer):
    # Tokenize the input
    tokens = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    input_ids = tokens["input_ids"]
    
    # Run inference
    model.eval()
    with torch.no_grad():
        outputs = model(input_ids, return_attention=True, return_dict=True)
    
    # Get prediction results
    logits = outputs["logits"]
    attention_weights = outputs["attention_weights"]
    
    # Convert to probabilities and get prediction
    probs = torch.nn.functional.softmax(logits, dim=1)
    prediction = torch.argmax(probs, dim=1).item()
    confidence = probs[0][prediction].item()
    sentiment = "Positive" if prediction == 1 else "Negative"
    
    # Get token strings for visualization
    tokens_list = []
    for id in input_ids[0]:
        token = tokenizer.convert_ids_to_tokens(id.item())
        tokens_list.append(token)
    
    # Create visualization
    fig, ax = plt.subplots(figsize=(10, 2))
    sns.heatmap(
        attention_weights.squeeze(0).cpu().numpy().reshape(1, -1),
        cmap="YlOrRd",
        annot=True,
        fmt=".2f",
        cbar=False,
        xticklabels=tokens_list,
        yticklabels=["Attention"],
        ax=ax
    )
    
    # Rotate x-axis labels for better readability
    plt.xticks(rotation=45, ha="right", rotation_mode="anchor")
    plt.title(f"Prediction: {sentiment} (Confidence: {confidence:.4f})")
    plt.tight_layout()
    
    # Convert plot to image
    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=150, bbox_inches="tight")
    buf.seek(0)
    img = Image.open(buf)
    plt.close(fig)
    
    return sentiment, confidence, img

# Streamlit app
def main():
    st.set_page_config(
        page_title="Sentiment Analysis with Attention",
        page_icon="🧠",
        layout="wide"
    )
    
    st.title("Sentiment Analysis with Attention Visualization")
    st.write("This model classifies text sentiment as positive or negative and visualizes which parts of the text it focused on using an attention mechanism.")
    
    # Load model and tokenizer
    try:
        model, tokenizer = load_model()
        model_loaded = True
    except Exception as e:
        st.error(f"Error loading model: {e}")
        model_loaded = False
    
    # Text input
    text_input = st.text_area(
        "Enter text to analyze:",
        value="I absolutely loved this movie! The acting was superb.",
        height=100,
    )
    
    # Process when button is clicked
    if st.button("Analyze Sentiment") and model_loaded:
        with st.spinner("Analyzing..."):
            sentiment, confidence, viz_img = predict_sentiment(text_input, model, tokenizer)
            
            # Display results
            col1, col2 = st.columns([1, 3])
            
            with col1:
                st.subheader("Prediction:")
                sentiment_color = "#5FD068" if sentiment == "Positive" else "#D21312"
                st.markdown(
                    f"<div style='background-color:{sentiment_color}; padding:10px; border-radius:5px;"
                    f"color:white; text-align:center; font-size:24px;'>{sentiment}</div>",
                    unsafe_allow_html=True
                )
                st.metric("Confidence", f"{confidence:.2%}")
            
            with col2:
                st.subheader("Attention Visualization:")
                st.image(viz_img, use_column_width=True)
                st.caption("The heatmap shows which words the model focused on most when making its prediction.")
            
            st.markdown("---")
            st.subheader("How to interpret the visualization:")
            st.write(
                "The attention heatmap shows the weight assigned to each token in the text. "
                "Darker colors indicate where the model focused more attention when making its prediction. "
                "This can help identify which parts of the text were most influential for sentiment classification."
            )

if __name__ == "__main__":
    main()