Create Sequence Classification.py

pages/Sequence Classification.py

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+import streamlit as st
+import sparknlp
+
+from sparknlp.base import *
+from sparknlp.annotator import *
+from pyspark.ml import Pipeline
+from annotated_text import annotated_text
+
+# Page configuration
+st.set_page_config(
+    layout="wide", 
+    initial_sidebar_state="auto"
+)
+
+# CSS for styling
+st.markdown("""
+    <style>
+        .main-title {
+            font-size: 36px;
+            color: #4A90E2;
+            font-weight: bold;
+            text-align: center;
+        }
+        .section {
+            background-color: #f9f9f9;
+            padding: 10px;
+            border-radius: 10px;
+            margin-top: 10px;
+        }
+        .section p, .section ul {
+            color: #666666;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+@st.cache_resource
+def init_spark():
+    return sparknlp.start()
+
+@st.cache_resource
+def create_pipeline():
+    document_assembler = DocumentAssembler() \
+        .setInputCol('text') \
+        .setOutputCol('document')
+
+    tokenizer = Tokenizer() \
+        .setInputCols(['document']) \
+        .setOutputCol('token')
+
+    sequence_classifier = XlmRoBertaForSequenceClassification.pretrained('xlm_roberta_base_sequence_classifier_imdb', 'en') \
+        .setInputCols(["document", "token"]) \
+        .setOutputCol("class")
+    
+    pipeline = Pipeline(stages=[document_assembler, tokenizer, sequence_classifier])
+    return pipeline
+
+def fit_data(pipeline, data):
+    empty_df = spark.createDataFrame([['']]).toDF('text')
+    pipeline_model = pipeline.fit(empty_df)
+    model = LightPipeline(pipeline_model)
+    result = model.fullAnnotate(data)
+    return result
+
+def annotate(data):
+    document, chunks, labels = data["Document"], data["NER Chunk"], data["NER Label"]
+    annotated_words = []
+    for chunk, label in zip(chunks, labels):
+        parts = document.split(chunk, 1)
+        if parts[0]:
+            annotated_words.append(parts[0])
+        annotated_words.append((chunk, label))
+        document = parts[1]
+    if document:
+        annotated_words.append(document)
+    annotated_text(*annotated_words)
+
+tasks_models_descriptions = {
+    "Sequence Classification": {
+        "models": ["xlm_roberta_base_sequence_classifier_imdb"],
+        "description": "The 'xlm_roberta_base_sequence_classifier_imdb' model is specialized for sentiment analysis of movie reviews. It accurately classifies IMDb reviews as positive or negative, leveraging the multilingual capabilities of XLM-RoBERTa to analyze text content and sentiment across different languages."
+    }
+}
+
+# Sidebar content
+task = st.sidebar.selectbox("Choose the task", list(tasks_models_descriptions.keys()))
+model = st.sidebar.selectbox("Choose the pretrained model", tasks_models_descriptions[task]["models"], help="For more info about the models visit: https://sparknlp.org/models")
+
+# Reference notebook link in sidebar
+link = """
+<a href="https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/357691d18373d6e8f13b5b1015137a398fd0a45f/Spark_NLP_Udemy_MOOC/Open_Source/17.01.Transformers-based_Embeddings.ipynb#L103">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" style="zoom: 1.3" alt="Open In Colab"/>
+</a>
+"""
+st.sidebar.markdown('Reference notebook:')
+st.sidebar.markdown(link, unsafe_allow_html=True)
+
+# Page content
+title, sub_title = (f'DeBERTa for {task}', tasks_models_descriptions[task]["description"])
+st.markdown(f'<div class="main-title">{title}</div>', unsafe_allow_html=True)
+container = st.container(border=True)
+container.write(sub_title)
+
+# Load examples
+examples_mapping = {
+    "Sequence Classification": [
+        "This movie was absolutely fantastic! The storyline was gripping, the characters were well-developed, and the cinematography was stunning. I was on the edge of my seat the entire time.",
+        "A heartwarming and beautiful film. The performances were top-notch, and the direction was flawless. This is easily one of the best movies I've seen this year.",
+        "What a delightful surprise! The humor was spot on, and the plot was refreshingly original. The cast did an amazing job bringing the characters to life. Highly recommended!",
+        "This was one of the worst movies I’ve ever seen. The plot was predictable, the acting was wooden, and the pacing was painfully slow. I couldn’t wait for it to end.",
+        "A complete waste of time. The movie lacked any real substance or direction, and the dialogue was cringe-worthy. I wouldn’t recommend this to anyone.",
+        "I had high hopes for this film, but it turned out to be a huge disappointment. The story was disjointed, and the special effects were laughably bad. Don’t bother watching this one.",
+        "The movie was okay, but nothing special. It had a few good moments, but overall, it felt pretty average. Not something I would watch again, but it wasn’t terrible either.",
+        "An average film with a decent plot. The acting was passable, but it didn't leave much of an impression on me. It's a movie you might watch once and forget about.",
+        "This movie was neither good nor bad, just kind of there. It had some interesting ideas, but they weren’t executed very well. It’s a film you could take or leave."
+    ]
+}
+
+examples = examples_mapping[task]
+selected_text = st.selectbox("Select an example", examples)
+custom_input = st.text_input("Try it with your own Sentence!")
+
+try:
+  text_to_analyze = custom_input if custom_input else selected_text
+  st.subheader('Full example text')
+  HTML_WRAPPER = """<div class="scroll entities" style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem; white-space:pre-wrap">{}</div>"""
+  st.markdown(HTML_WRAPPER.format(text_to_analyze), unsafe_allow_html=True)
+except:
+  text_to_analyze = selected_text
+
+# Initialize Spark and create pipeline
+spark = init_spark()
+pipeline = create_pipeline()
+output = fit_data(pipeline, text_to_analyze)
+
+# Display matched sentence
+st.subheader("Prediction:")
+st.markdown(f"Classified as : **{output[0]['class'][0].result}**")