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Create app.py
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app.py
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import streamlit as st
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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import torch
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import difflib
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# Load the grammar correction model
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@st.cache_resource
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def load_model():
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model_name = "prithivida/grammar_error_correcter_v1"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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return tokenizer, model
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tokenizer, model = load_model()
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# Function to correct grammar
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def correct_grammar(text):
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input_text = "gec: " + text
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inputs = tokenizer.encode(input_text, return_tensors="pt", truncation=True)
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outputs = model.generate(inputs, max_length=512, num_beams=4, early_stopping=True)
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corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return corrected_text
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# Function to show differences
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def show_differences(original, corrected):
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diff = difflib.ndiff(original.split(), corrected.split())
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changes = []
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for d in diff:
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if d.startswith("- "):
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changes.append(f"β Removed: `{d[2:]}`")
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elif d.startswith("+ "):
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changes.append(f"β
Added: `{d[2:]}`")
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return "\n".join(changes) if changes else "No major changes found."
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# Streamlit UI
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st.title("π Grammar Correction App")
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st.write("Enter a sentence or paragraph below, and the AI will correct grammatical errors and highlight the changes.")
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user_input = st.text_area("Your Text", height=200, placeholder="Type or paste your text here...")
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if st.button("Correct Grammar"):
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if user_input.strip():
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with st.spinner("Correcting grammar..."):
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corrected = correct_grammar(user_input)
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st.subheader("β
Corrected Text")
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st.success(corrected)
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st.subheader("π΅οΈ What Changed?")
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st.markdown(show_differences(user_input, corrected))
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else:
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st.warning("Please enter some text to correct.")
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