goodies/sentiment.py

import streamlit as st
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import requests
from bs4 import BeautifulSoup
import pandas as pd
import altair as alt
from collections import OrderedDict
from nltk.tokenize import sent_tokenize

# Load the punkt tokenizer from nltk
import nltk
nltk.download('punkt')

# Load model and tokenizer
model_name = 'dejanseo/sentiment'
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Sentiment labels as textual descriptions
sentiment_labels = {
    0: "very positive",
    1: "positive",
    2: "somewhat positive",
    3: "neutral",
    4: "somewhat negative",
    5: "negative",
    6: "very negative"
}

# Background colors for sentiments
background_colors = {
    "very positive": "rgba(0, 255, 0, 0.5)",
    "positive": "rgba(0, 255, 0, 0.3)",
    "somewhat positive": "rgba(0, 255, 0, 0.1)",
    "neutral": "rgba(128, 128, 128, 0.1)",
    "somewhat negative": "rgba(255, 0, 0, 0.1)",
    "negative": "rgba(255, 0, 0, 0.3)",
    "very negative": "rgba(255, 0, 0, 0.5)"
}

# Function to get text content from a URL
def get_text_from_url(url):
    response = requests.get(url)
    if response.status_code == 200:
        soup = BeautifulSoup(response.content, 'html.parser')
        paragraphs = soup.find_all('p')
        return ' '.join(p.get_text() for p in paragraphs)
    return ""

# Function to classify text
def classify_text(text, max_length):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=max_length)
    with torch.no_grad():
        outputs = model(**inputs)
    scores = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()
    return scores

# Function to handle long texts
def classify_long_text(text):
    max_length = tokenizer.model_max_length
    # Split the text into chunks
    chunks = [text[i:i + max_length] for i in range(0, len(text), max_length)]
    aggregate_scores = [0] * len(sentiment_labels)
    chunk_scores_list = []
    for chunk in chunks:
        chunk_scores = classify_text(chunk, max_length)
        chunk_scores_list.append(chunk_scores)
        aggregate_scores = [x + y for x, y in zip(aggregate_scores, chunk_scores)]
    # Average the scores
    aggregate_scores = [x / len(chunks) for x in aggregate_scores]
    return aggregate_scores, chunk_scores_list, chunks

# Function to classify each sentence in the text
def classify_sentences(text):
    sentences = sent_tokenize(text)
    sentence_scores = []
    for sentence in sentences:
        scores = classify_text(sentence, tokenizer.model_max_length)
        sentiment_idx = scores.index(max(scores))
        sentiment = sentiment_labels[sentiment_idx]
        sentence_scores.append((sentence, sentiment))
    return sentence_scores

# Streamlit UI
st.title("Sentiment Classification from URL")

url = st.text_input("Enter URL:")

# Additional information
st.markdown("""
Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).

The model is designed to be deployed in an automated pipeline capable of classifying text sentiment for thousands (or even millions) of text chunks or as a part of a scraping pipeline.

This is a demo model which may occassionally misclasify some texts. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.

# Engage Our Team
Interested in using this in an automated pipeline for bulk query processing?

Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs.
""")

if url:
    text = get_text_from_url(url)
    if text:
        scores, chunk_scores_list, chunks = classify_long_text(text)
        scores_dict = {sentiment_labels[i]: scores[i] for i in range(len(sentiment_labels))}
        
        # Ensure the exact order of labels in the graph
        sentiment_order = [
            "very positive", "positive", "somewhat positive",
            "neutral",
            "somewhat negative", "negative", "very negative"
        ]
        ordered_scores_dict = OrderedDict((label, scores_dict[label]) for label in sentiment_order)
        
        # Prepare the DataFrame and reindex
        df = pd.DataFrame.from_dict(ordered_scores_dict, orient='index', columns=['Likelihood']).reindex(sentiment_order)
        
        # Use Altair to plot the bar chart
        chart = alt.Chart(df.reset_index()).mark_bar().encode(
            x=alt.X('index', sort=sentiment_order, title='Sentiment'),
            y='Likelihood'
        ).properties(
            width=600,
            height=400
        )

        st.altair_chart(chart, use_container_width=True)

        # Display each chunk and its own chart
        for i, (chunk_scores, chunk) in enumerate(zip(chunk_scores_list, chunks)):
            chunk_scores_dict = {sentiment_labels[j]: chunk_scores[j] for j in range(len(sentiment_labels))}
            ordered_chunk_scores_dict = OrderedDict((label, chunk_scores_dict[label]) for label in sentiment_order)
            df_chunk = pd.DataFrame.from_dict(ordered_chunk_scores_dict, orient='index', columns=['Likelihood']).reindex(sentiment_order)
            
            chunk_chart = alt.Chart(df_chunk.reset_index()).mark_bar().encode(
                x=alt.X('index', sort=sentiment_order, title='Sentiment'),
                y='Likelihood'
            ).properties(
                width=600,
                height=400
            )

            st.write(f"Chunk {i + 1}:")
            st.write(chunk)
            st.altair_chart(chunk_chart, use_container_width=True)

        # Sentence-level classification with background colors
        st.write("Extracted Text with Sentiment Highlights:")
        sentence_scores = classify_sentences(text)
        for sentence, sentiment in sentence_scores:
            bg_color = background_colors[sentiment]
            st.markdown(f'<span style="background-color: {bg_color}">{sentence}</span>', unsafe_allow_html=True)

    else:
        st.write("Could not extract text from the provided URL.")

# Additional information at the end
st.markdown("""
Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).

The model is designed to be deployed in an automated pipeline capable of classifying text sentiment for thousands (or even millions) of text chunks or as a part of a scraping pipeline. This is a demo model which may occassionally misclasify some texts. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.

### Engage Our Team
Interested in using this in an automated pipeline for bulk query processing?

Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs.
""")