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import streamlit as st |
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import transformers |
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import tensorflow |
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import PIL |
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from PIL import Image |
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import time |
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from transformers import pipeline |
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model_checkpoint = "Modfiededition/t5-base-fine-tuned-on-jfleg" |
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@st.cache(allow_output_mutation=True, suppress_st_warning=True) |
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def load_model(): |
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return pipeline("text2text-generation", model=model_checkpoint) |
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model = load_model() |
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st.title("Writing Assistant for you π€") |
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st.markdown("This writing assistant detects and corrects grammatical mistakes for you! This assitant uses **T5-base model βοΈ** fine-tuned on jfleg dataset.") |
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st.subheader("Some examples: ") |
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example_1 = st.button("I am write on AI") |
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example_2 = st.button("This sentence has, bads grammar mistake!") |
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textbox = st.text_area('Write your text in this box:', '',height=100, max_chars=500 ) |
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button = st.button('Detect grammar mistakes:') |
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st.subheader("Correct sentence: ") |
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if example_1: |
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with st.spinner('In progress.......'): |
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output_text = model("I am write on AI")[0]["generated_text"] |
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st.markdown("## "+output_text) |
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if example_2: |
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with st.spinner('In progress.......'): |
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output_text = model("This sentence has, bads grammar mistake!")[0]["generated_text"] |
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st.markdown("## "+output_text) |
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if button: |
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with st.spinner('In progress.......'): |
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if textbox: |
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output_text = model(textbox)[0]["generated_text"] |
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else: |
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output_text = " " |
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st.markdown("## "+output_text) |
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