Delete Demo.py

Demo.py

@@ -1,121 +0,0 @@
-import streamlit as st
-import sparknlp
-import os
-import pandas as pd
-
-from sparknlp.base import *
-from sparknlp.annotator import *
-from pyspark.ml import Pipeline
-from sparknlp.pretrained import PretrainedPipeline
-
-# Page configuration
-st.set_page_config(
-    layout="wide", 
-    page_title="Spark NLP Demos App", 
-    initial_sidebar_state="auto"
-)
-
-# CSS for styling
-st.markdown("""
-    <style>
-        .main-title {
-            font-size: 36px;
-            color: #4A90E2;
-            font-weight: bold;
-            text-align: center;
-        }
-        .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 = DocumentAssembler() \
-            .setInputCol("text") \
-            .setOutputCol("document")
-
-    # Step 2: Sentence Detection
-    sentenceDetector = SentenceDetector() \
-                .setInputCols("document") \
-                .setOutputCol("sentences")
-
-    # Step 3: Tokenization
-    token = Tokenizer() \
-                .setInputCols("sentences") \
-                .setOutputCol("tokens") \
-                .setContextChars(["(", ")", "?", "!", ".", ","])
-
-    # Step 4: Coreference Resolution
-    corefResolution = SpanBertCorefModel().pretrained("spanbert_base_coref") \
-                .setInputCols(["sentences", "tokens"]) \
-                .setOutputCol("corefs") \
-                .setCaseSensitive(False)
-
-    # Define the pipeline
-    pipeline = Pipeline(stages=[document, sentenceDetector, token, corefResolution])
-
-def fit_data(pipeline, data):
-    empty_df = spark.createDataFrame([['']]).toDF('text')
-    pipeline_model = pipeline.fit(empty_df)
-    model = LightPipeline(pipeline_model)
-    results = model.fullAnnotate(data)
-    return results
-
-# Set up the page layout
-st.markdown('<div class="main-title">State-of-the-Art Coreference Resolution in Spark NLP</div>', unsafe_allow_html=True)
-
-# Sidebar content
-model_name = st.sidebar.selectbox(
-    "Choose the pretrained model",
-    ['spanbert_base_coref'],
-    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/blob/master/examples/python/annotation/text/english/coreference-resolution/Coreference_Resolution_SpanBertCorefModel.ipynb#L117">
-    <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)
-
-# Load examples
-examples = [
-    "Alice went to the market. She bought some fresh vegetables there. The tomatoes she purchased were particularly ripe.",
-    "Dr. Smith is a renowned surgeon. He has performed over a thousand successful operations. His colleagues respect him a lot.",
-    "The company announced a new product launch. It is expected to revolutionize the industry. The CEO was very excited about it.",
-    "Jennifer enjoys hiking. She goes to the mountains every weekend. Her favorite spot is the Blue Ridge Mountains.",
-    "The team won the championship. They celebrated their victory with a huge party. Their coach praised their hard work and dedication.",
-    "Michael is studying computer science. He finds artificial intelligence fascinating. His dream is to work at a leading tech company.",
-    "The book was well-received by critics. It was praised for its intricate plot and well-developed characters. The author felt proud of his work.",
-    "Sarah adopted a kitten. She named it Whiskers. Whiskers loves to play with her and often follows her around the house.",
-    "The project was completed ahead of schedule. It was a collaborative effort. The team members were rewarded for their contribution.",
-    "Tom is a skilled guitarist. He plays in a local band. His performances are always energetic and captivating."
-]
-
-# st.subheader("Automatically detect phrases expressing dates and normalize them with respect to a reference date.")
-selected_text = st.selectbox("Select an example", examples)
-custom_input = st.text_input("Try it with your own Sentence!")
-
-text_to_analyze = custom_input if custom_input else selected_text
-
-st.subheader('Full example text')
-st.write(text_to_analyze)
-
-# Initialize Spark and create pipeline
-spark = init_spark()
-pipeline = create_pipeline()
-output = fit_data(pipeline, text_to_analyze)
-
-# Display matched sentence
-st.subheader("Processed output:")
-df = extract_to_dataframe(output)
-df.index += 1
-st.dataframe(df)