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Update app.py
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app.py
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import streamlit as st
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from transformers import pipeline
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st.title("English to Levantine Arabic")
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num_translations = st.sidebar.selectbox("Number of Translations:", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], index=
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input_text = st.text_input("Enter English text:")
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if input_text:
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import streamlit as st
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from transformers import pipeline
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import pandas as pd
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dialects = {"Palestinian/Jordanian": "P", "Syrian": "S", "Lebanese": "L", "Egyptian": "E"}
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pipeline = pipeline(task="translation", model="guymorlan/English2Dialect")
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st.title("English to Levantine Arabic")
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num_translations = st.sidebar.selectbox("Number of Translations Per Dialect:", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], index=0)
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input_text = st.text_input("Enter English text:")
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if input_text:
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inputs = [f"{val} {input_text}" for val in dialects.values()]
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result = pipeline(inputs, max_length=1024, num_return_sequences=num_translations, num_beams=max(num_translations, 5))
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#df = pd.DataFrame({"Dialect": [x for x in dialects.keys()],
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# "Translation": [x["translation_text"] for x in result]})
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for i in range(len(result)):
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st.markdown(f"<div style='font-size:24px'><b>{list(dialects.keys())[i]}:</b></div>", unsafe_allow_html=True)
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if num_translations > 1:
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for j in range(num_translations):
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st.markdown(f"<div style='font-size:24px; text-align:right; direction:rtl;'>{result[i][j]['translation_text']}</div>", unsafe_allow_html=True)
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else:
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st.markdown(f"<div style='font-size:24px; text-align:right; direction:rtl;'>{result[i]['translation_text']}</div>", unsafe_allow_html=True)
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st.markdown("<br>", unsafe_allow_html=True)
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