Delete app.py

app.py

@@ -1,46 +0,0 @@
-import streamlit as st
-import nltk
-from nltk.tree import Tree
-from nltk import pos_tag, word_tokenize
-from nltk.parse import CoreNLPParser
-import os
-import base64
-from graphviz import Source
-
-# Ensure necessary NLTK resources are downloaded
-nltk.download('punkt')
-nltk.download('averaged_perceptron_tagger')
-
-# Define a function to generate and render a syntax tree
-def generate_syntax_tree(sentence):
-    # Tokenize and part-of-speech tag the sentence
-    tokens = word_tokenize(sentence)
-    tagged_tokens = pos_tag(tokens)
-
-    # Use NLTK's CoreNLPParser to parse the sentence
-    parser = CoreNLPParser(url='http://localhost:9000')  # Make sure CoreNLP server is running
-    parsed_tree = next(parser.parse(tagged_tokens))
-
-    # Convert the tree to a DOT format for visualization
-    dot_format = parsed_tree._repr_svg_()
-    return dot_format
-
-# Function to display syntax tree image
-def display_tree(dot_format):
-    src = Source(dot_format)
-    svg = src.pipe(format='svg')
-    svg_base64 = base64.b64encode(svg).decode('utf-8')
-    svg_html = f"<div style='width:100%;height:100%;'><img src='data:image/svg+xml;base64,{svg_base64}'></div>"
-    return svg_html
-
-# Set up the Streamlit interface
-st.title("Syntax Tree Generator for English")
-sentence = st.text_input("Enter a sentence:", "")
-
-if sentence:
-    try:
-        dot_format = generate_syntax_tree(sentence)
-        svg_html = display_tree(dot_format)
-        st.markdown(svg_html, unsafe_allow_html=True)
-    except Exception as e:
-        st.error(f"An error occurred: {e}")