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import streamlit as st |
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import time |
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from sincode_model import BeamSearchDecoder |
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from PIL import Image |
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import base64 |
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st.set_page_config(page_title="සිංCode Prototype", page_icon="🇱🇰", layout="centered") |
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def add_bg_from_local(image_file): |
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try: |
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with open(image_file, "rb") as f: |
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data = f.read() |
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b64_data = base64.b64encode(data).decode() |
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st.markdown( |
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f""" |
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<style> |
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.stApp {{ |
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background-image: linear-gradient(rgba(0,0,0,0.7), rgba(0,0,0,0.7)), url(data:image/png;base64,{b64_data}); |
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background-size: cover; |
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background-position: center; |
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background-attachment: fixed; |
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}} |
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</style> |
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""", |
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unsafe_allow_html=True |
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) |
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except FileNotFoundError: |
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pass |
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@st.cache_resource |
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def load_system(): |
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decoder = BeamSearchDecoder() |
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return decoder |
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background_path = "images/background.png" |
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add_bg_from_local(background_path) |
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with st.sidebar: |
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logo = Image.open("images/SinCodeLogo.jpg") |
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st.image(logo, width=200) |
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st.title("සිංCode Project") |
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st.info("Prototype") |
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st.markdown("### 🏗 Architecture") |
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st.success(""" |
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**Hybrid Neuro-Symbolic Engine** |
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Combines rule-based speed with Deep Learning (XLM-R) context awareness. |
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**Adaptive Code-Switching** |
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Intelligently detects and preserves English contexts. |
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**Contextual Disambiguation** |
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Resolves Singlish ambiguity using sentence-level probability. |
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""") |
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st.markdown("---") |
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st.write("© 2026 Kalana Chandrasekara") |
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st.title("සිංCode: Context-Aware Transliteration") |
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st.markdown("Type Singlish sentences below. The system handles **code-mixing**, **ambiguity**, and **punctuation**.") |
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input_text = st.text_area("Input Text", height=100, placeholder="e.g., Singlish sentences type krnna") |
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if st.button("Transliterate", type="primary", use_container_width=True) and input_text: |
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try: |
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with st.spinner("Processing..."): |
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decoder = load_system() |
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start_time = time.time() |
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result, trace_logs = decoder.decode(input_text) |
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end_time = time.time() |
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st.success("Transliteration Complete") |
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st.markdown(f"### {result}") |
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st.caption(f"Time: {round(end_time - start_time, 2)}s") |
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with st.expander("See How It Works (Debug Info)", expanded=True): |
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st.write("Below shows the candidate scoring for each word step:") |
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for log in trace_logs: |
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st.markdown(log) |
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st.divider() |
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except Exception as e: |
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st.error(f"Error: {e}") |
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