src/streamlit_app.py

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

# # Configure nltk to use /tmp
# nltk_data_path = "/tmp/nltk_data"
# nltk.data.path.append(nltk_data_path)
# nltk.download('punkt', download_dir=nltk_data_path)

# # Load installed spaCy model
# nlp = spacy.load("en_core_web_sm")

# # Add benepar parser
# if "benepar" not in nlp.pipe_names:
#     benepar.download("benepar_en3")
#     nlp.add_pipe("benepar", config={"model": "benepar_en3"})
# # Streamlit UI
# st.set_page_config(page_title="Syntax Parser Comparison", layout="wide")
# st.title("🌐 Syntax Parser Comparison Tool")
# st.write("This tool compares Dependency Parsing, Constituency Parsing, and a simulated Abstract Syntax Representation (ASR).")

# # Input
# 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
import nltk
import spacy
import benepar
from nltk import Tree
from graphviz import Digraph

# Configure NLTK to use /tmp for downloads
nltk_data_path = "/tmp/nltk_data"
nltk.data.path.append(nltk_data_path)
nltk.download('punkt', download_dir=nltk_data_path)

# Load spaCy model
nlp = spacy.load("en_core_web_sm")

# Add benepar if not already added
if "benepar" not in nlp.pipe_names:
    benepar.download("benepar_en3")
    nlp.add_pipe("benepar", config={"model": "benepar_en3"})

# Streamlit page config
st.set_page_config(
    page_title="🌐 Syntax Parser Comparison Tool",
    page_icon="📝",
    layout="wide"
)

# Sidebar Info
st.sidebar.title("ℹ️ About This Tool")
st.sidebar.write("""
Compare **Dependency Parsing**, **Constituency Parsing**,  
and a simulated **Abstract Syntax Representation (ASR)**.
""")
st.sidebar.markdown("---")
st.sidebar.info("💡 Enter a sentence in the input box to see all 3 parses.")

# Title
st.title("🌐 Syntax Parser Comparison Tool")
st.markdown("""
This tool demonstrates **three parsing styles** side-by-side:
1. **Dependency Parsing** – Shows head–dependent word relationships.
2. **Constituency Parsing** – Displays hierarchical phrase structures.
3. **Abstract Syntax Representation (ASR)** – Merges phrase structure with dependency info.
""")

# Input sentence
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)

    # 1️⃣ Dependency Parsing
    with col1:
        st.subheader("🔗 Dependency Parsing")
        dep_graph = Digraph()
        dep_graph.attr(rankdir="TB")
        for token in sent:
            dep_graph.node(token.text, f"{token.text}\n({token.dep_})")
            if token.head != token:
                dep_graph.edge(token.head.text, token.text)
        st.graphviz_chart(dep_graph)
        with st.expander("Raw Dependency Tuples"):
            st.code(" ".join(f"({t.text}, {t.dep_}, {t.head.text})" for t in sent))

    # 2️⃣ Constituency Parsing
    with col2:
        st.subheader("🌳 Constituency Parsing")
        tree_str = sent._.parse_string
        with st.expander("Tree String"):
            st.text(tree_str)
        st.code(Tree.fromstring(tree_str).pformat(), language="text")

    # 3️⃣ Simulated ASR
    with col3:
        st.subheader("🧩 Simulated ASR Output")
        st.markdown("Combines **dependency heads**, **POS tags**, and **phrase info**.")
        highlighted_output = []
        for token in sent:
            if token.dep_ in ("nsubj", "obj", "det", "ROOT"):
                highlighted_output.append(
                    f"**[{token.text}]** - {token.dep_} → {token.head.text} ({token.pos_})"
                )
        st.write("\n".join(highlighted_output))
        with st.expander("ASR Encoded String"):
            st.code(
                " ".join(f"[{t.text}: {t.dep_} → {t.head.text}]({t.pos_})" for t in sent)
            )