app.py

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

# 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):
    downloaded = trafilatura.fetch_url(url)
    if downloaded:
        return trafilatura.extract(downloaded)
    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 Model by DEJAN")

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

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 sentiment processing?

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