streamlit_app.py

# import os
# os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"

# import streamlit as st
# import spacy
# import benepar
# from nltk import Tree
# import nltk

# # Setup NLTK and benepar
# nltk.download('punkt')
# benepar.download('benepar_en3')

# nlp = spacy.load("en_core_web_sm")
# if "benepar" not in nlp.pipe_names:
#     nlp.add_pipe("benepar", config={"model": "benepar_en3"})

# st.set_page_config(page_title="Syntax Parser Comparison Tool", layout="wide")
# st.title("🌐 Syntax Parser Comparison Tool")
# st.write("This tool compares Dependency Parsing, Constituency Parsing, and a simulated Abstract Syntax Representation (ASR).")

# sentence = st.text_input("Enter a sentence:", "John eats an apple.")

# if sentence:
#     doc = nlp(sentence)
#     sent = list(doc.sents)[0]

#     col1, col2, col3 = st.columns(3)

#     with col1:
#         st.header("Dependency Parsing")
#         for token in sent:
#             st.write(f"{token.text} --> {token.dep_} --> {token.head.text}")
#         st.code(" ".join(f"({token.text}, {token.dep_}, {token.head.text})" for token in sent))

#     with col2:
#         st.header("Constituency Parsing")
#         tree = sent._.parse_string
#         st.text(tree)
#         st.code(Tree.fromstring(tree).pformat())

#     with col3:
#         st.header("Simulated ASR Output")
#         st.write("Combining phrase structure with dependency head annotations:")
#         for token in sent:
#             if token.dep_ in ("nsubj", "obj", "det", "ROOT"):
#                 st.write(f"[{token.text}] - {token.dep_} --> {token.head.text} ({token.pos_})")
#         st.markdown("_(ASR is simulated by combining POS tags, dependency heads, and phrase information.)_")
#         st.code(" ".join(f"[{token.text}: {token.dep_} → {token.head.text}]({token.pos_})" for token in sent))


import streamlit as st

st.title("✅ Custom App Loaded")
st.write("If you can see this, your `streamlit_app.py` is correctly loaded by Hugging Face.")