Create app.py

app.py

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+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
+import nltk
+from nltk.tokenize import sent_tokenize
+
+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:")
+
+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.
+""")