Delete pages/Sequence Classification.py

pages/Sequence Classification.py

@@ -1,137 +0,0 @@
-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}**")