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Build error
Build error
Add initial implementation of Grammar Correction tool with Flask and Streamlit
Browse files- .gitattributes +0 -35
- .gitignore +1 -0
- Dataset/C4_200M/c4data.csv +0 -0
- Dataset/JFLEG/eval.csv +0 -0
- Dataset/JFLEG/train.csv +0 -0
- Dataset/eval.csv +0 -0
- Dataset/train.csv +0 -0
- Grammar_Correction.ipynb +2415 -0
- README.md +75 -1
- app.py +25 -0
- dataset_preprocess.py +68 -0
- model.py +22 -0
- requirements.txt +7 -0
- static/style.css +120 -0
- streamlit_app.py +42 -0
- templates/index.html +38 -0
.gitattributes
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Dataset/C4_200M/c4data.csv
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Dataset/JFLEG/eval.csv
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Dataset/eval.csv
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Dataset/train.csv
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Grammar_Correction.ipynb
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 3,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"name": "stdout",
|
| 10 |
+
"output_type": "stream",
|
| 11 |
+
"text": [
|
| 12 |
+
"Collecting happytransformer\n",
|
| 13 |
+
" Using cached happytransformer-3.0.0-py3-none-any.whl.metadata (4.4 kB)\n",
|
| 14 |
+
"Requirement already satisfied: matplotlib in c:\\python312\\lib\\site-packages (3.9.2)\n",
|
| 15 |
+
"Collecting torch>=1.0 (from happytransformer)\n",
|
| 16 |
+
" Using cached torch-2.5.1-cp312-cp312-win_amd64.whl.metadata (28 kB)\n",
|
| 17 |
+
"Requirement already satisfied: tqdm>=4.43 in c:\\python312\\lib\\site-packages (from happytransformer) (4.67.1)\n",
|
| 18 |
+
"Collecting transformers<5.0.0,>=4.30.1 (from happytransformer)\n",
|
| 19 |
+
" Using cached transformers-4.48.1-py3-none-any.whl.metadata (44 kB)\n",
|
| 20 |
+
"Collecting datasets<3.0.0,>=2.13.1 (from happytransformer)\n",
|
| 21 |
+
" Using cached datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n",
|
| 22 |
+
"Requirement already satisfied: sentencepiece in c:\\python312\\lib\\site-packages (from happytransformer) (0.2.0)\n",
|
| 23 |
+
"Requirement already satisfied: protobuf in c:\\python312\\lib\\site-packages (from happytransformer) (5.29.3)\n",
|
| 24 |
+
"Collecting accelerate<1.0.0,>=0.20.1 (from happytransformer)\n",
|
| 25 |
+
" Using cached accelerate-0.34.2-py3-none-any.whl.metadata (19 kB)\n",
|
| 26 |
+
"Collecting tokenizers<1.0.0,>=0.13.3 (from happytransformer)\n",
|
| 27 |
+
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"c:\\Users\\amanu\\projects\\nlp\\aman\\env\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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"import pickle\n",
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"import matplotlib.pyplot as plt\n",
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"from happytransformer import HappyTextToText, TTTrainArgs"
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"source": [
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"happy_tt = HappyTextToText(\"T5\", \"t5-base\")"
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"01/26/2025 00:28:57 - INFO - happytransformer.happy_transformer - Preprocessing dataset...\n"
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"eval_args = TTTrainArgs(\n",
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" batch_size=1,\n",
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"print(\"Evaluating the model BEFORE training...\")\n",
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"eval_results_before = happy_tt.eval(\"Dataset/eval.csv\", args=eval_args)\n",
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| 228 |
+
"print(\"Evaluation Loss (Before Training):\", eval_results_before.loss)\n",
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" f.write(str(eval_results_before))\n"
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" Train Model"
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"outputs": [],
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"source": [
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"train_args = TTTrainArgs(\n",
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+
" batch_size=8,\n",
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" num_train_epochs=5,\n",
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+
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" eval_ratio=0.1\n",
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")\n",
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+
"print(\"Training the model...\")\n",
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+
"training_losses = happy_tt.train(\"Dataset/train.csv\", args=train_args)"
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+
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+
{
|
| 261 |
+
"cell_type": "markdown",
|
| 262 |
+
"metadata": {},
|
| 263 |
+
"source": [
|
| 264 |
+
"Evaluate the model on the test set"
|
| 265 |
+
]
|
| 266 |
+
},
|
| 267 |
+
{
|
| 268 |
+
"cell_type": "code",
|
| 269 |
+
"execution_count": null,
|
| 270 |
+
"metadata": {},
|
| 271 |
+
"outputs": [],
|
| 272 |
+
"source": [
|
| 273 |
+
"print(\"Evaluating the model AFTER training...\")\n",
|
| 274 |
+
"eval_results_after = happy_tt.eval(\"Dataset/eval.csv\", args=eval_args)\n",
|
| 275 |
+
"eval_loss_after = eval_results_after['eval_loss']\n",
|
| 276 |
+
"print(\"Evaluation Loss (After Training):\", eval_loss_after)\n",
|
| 277 |
+
"\n",
|
| 278 |
+
"with open(\"eval_results_after.txt\", \"w\") as f:\n",
|
| 279 |
+
" f.write(\"Evaluation Results After Training:\\n\")\n",
|
| 280 |
+
" f.write(str(eval_results_after))\n"
|
| 281 |
+
]
|
| 282 |
+
},
|
| 283 |
+
{
|
| 284 |
+
"cell_type": "markdown",
|
| 285 |
+
"metadata": {},
|
| 286 |
+
"source": [
|
| 287 |
+
"Plot Results"
|
| 288 |
+
]
|
| 289 |
+
},
|
| 290 |
+
{
|
| 291 |
+
"cell_type": "code",
|
| 292 |
+
"execution_count": null,
|
| 293 |
+
"metadata": {},
|
| 294 |
+
"outputs": [],
|
| 295 |
+
"source": [
|
| 296 |
+
"epochs = list(range(1, len(training_losses) + 1))\n",
|
| 297 |
+
"plt.figure(figsize=(10, 6))\n",
|
| 298 |
+
"plt.plot(epochs, training_losses, label=\"Training Loss\", marker='o')\n",
|
| 299 |
+
"plt.axhline(y=eval_loss_before, color='orange', linestyle='--', label=f\"Evaluation Loss Before ({eval_loss_before:.4f})\")\n",
|
| 300 |
+
"plt.axhline(y=eval_loss_after, color='green', linestyle='--', label=f\"Evaluation Loss After ({eval_loss_after:.4f})\")\n",
|
| 301 |
+
"plt.xlabel(\"Epoch\")\n",
|
| 302 |
+
"plt.ylabel(\"Loss\")\n",
|
| 303 |
+
"plt.title(\"Training and Evaluation Losses\")\n",
|
| 304 |
+
"plt.legend()\n",
|
| 305 |
+
"plt.grid(True)\n",
|
| 306 |
+
"\n",
|
| 307 |
+
"plt.savefig(\"training_eval_comparison.png\")\n",
|
| 308 |
+
"plt.show()\n"
|
| 309 |
+
]
|
| 310 |
+
},
|
| 311 |
+
{
|
| 312 |
+
"cell_type": "markdown",
|
| 313 |
+
"metadata": {},
|
| 314 |
+
"source": [
|
| 315 |
+
"Final Results"
|
| 316 |
+
]
|
| 317 |
+
},
|
| 318 |
+
{
|
| 319 |
+
"cell_type": "code",
|
| 320 |
+
"execution_count": null,
|
| 321 |
+
"metadata": {},
|
| 322 |
+
"outputs": [],
|
| 323 |
+
"source": [
|
| 324 |
+
"print(\"\\nFinal Results:\")\n",
|
| 325 |
+
"print(f\"Evaluation Loss Before Training: {eval_loss_before:.4f}\")\n",
|
| 326 |
+
"print(f\"Evaluation Loss After Training: {eval_loss_after:.4f}\")"
|
| 327 |
+
]
|
| 328 |
+
}
|
| 329 |
+
],
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| 330 |
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| 331 |
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"accelerator": "GPU",
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| 332 |
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| 2413 |
+
"nbformat": 4,
|
| 2414 |
+
"nbformat_minor": 0
|
| 2415 |
+
}
|
README.md
CHANGED
|
@@ -11,4 +11,78 @@ license: mit
|
|
| 11 |
short_description: Grammar Correction for NLP course
|
| 12 |
---
|
| 13 |
|
| 14 |
-
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
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|
|
|
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|
|
|
|
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|
|
|
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|
|
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|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
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|
|
|
|
|
| 11 |
short_description: Grammar Correction for NLP course
|
| 12 |
---
|
| 13 |
|
| 14 |
+
---
|
| 15 |
+
|
| 16 |
+
**NLP Project**
|
| 17 |
+
|
| 18 |
+
This repository contains a Grammar Correction tool developed as part of the NLP project by **Software Group 2**. The tool leverages NLP techniques to detect and correct grammatical mistakes in user-provided text. It is designed to be user-friendly and provides two options for running the application: a Streamlit-based interactive app or a traditional web-based app.
|
| 19 |
+
|
| 20 |
+
---
|
| 21 |
+
|
| 22 |
+
## **Team Members**
|
| 23 |
+
|
| 24 |
+
- **AMANUEL YIHUNE HIBSTE** - UGR/8408/13
|
| 25 |
+
- **ARYAM WUBSHET BERHANU** - UGR/6357/13
|
| 26 |
+
- **BASLIEL AMSALU GELETU** - UGR/8569/13
|
| 27 |
+
- **BEREKET LEGESSE TADESSE** - UGR/7987/13
|
| 28 |
+
- **BETSELOT KIDANE BONSA** - UGR/8473/13
|
| 29 |
+
|
| 30 |
+
---
|
| 31 |
+
|
| 32 |
+
## **How to Run Locally**
|
| 33 |
+
|
| 34 |
+
### **Step 1: Clone the Repository**
|
| 35 |
+
|
| 36 |
+
```bash
|
| 37 |
+
git clone
|
| 38 |
+
cd repo
|
| 39 |
+
```
|
| 40 |
+
|
| 41 |
+
### **Step 2: Create a Virtual Environment**
|
| 42 |
+
|
| 43 |
+
Create a virtual environment to manage dependencies:
|
| 44 |
+
|
| 45 |
+
```bash
|
| 46 |
+
python -m venv env
|
| 47 |
+
.\env\Scripts\activate
|
| 48 |
+
```
|
| 49 |
+
|
| 50 |
+
### **Step 3: Install Requirements**
|
| 51 |
+
|
| 52 |
+
Install all necessary dependencies:
|
| 53 |
+
|
| 54 |
+
```bash
|
| 55 |
+
pip install -r requirements.txt
|
| 56 |
+
```
|
| 57 |
+
|
| 58 |
+
### **Step 4: Train the Model**
|
| 59 |
+
|
| 60 |
+
Run the training script to prepare the model:
|
| 61 |
+
|
| 62 |
+
```bash
|
| 63 |
+
python model.py
|
| 64 |
+
```
|
| 65 |
+
|
| 66 |
+
### **Step 5: Run the App**
|
| 67 |
+
|
| 68 |
+
#### **Option 1: Run as a Traditional Web App**
|
| 69 |
+
|
| 70 |
+
To run the app locally and access it via an HTML page:
|
| 71 |
+
|
| 72 |
+
```bash
|
| 73 |
+
python app.py
|
| 74 |
+
```
|
| 75 |
+
|
| 76 |
+
#### **Option 2: Run as a Streamlit App**
|
| 77 |
+
|
| 78 |
+
For an interactive Streamlit-based application:
|
| 79 |
+
|
| 80 |
+
```bash
|
| 81 |
+
streamlit run streamlit_app.py
|
| 82 |
+
```
|
| 83 |
+
|
| 84 |
+
---
|
| 85 |
+
|
| 86 |
+
## **Features**
|
| 87 |
+
|
| 88 |
+
- Grammar error detection and correction
|
app.py
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
from flask import Flask, request, jsonify, render_template
|
| 3 |
+
import pickle
|
| 4 |
+
|
| 5 |
+
from happytransformer import TTSettings
|
| 6 |
+
|
| 7 |
+
app = Flask(__name__)
|
| 8 |
+
|
| 9 |
+
@app.route('/')
|
| 10 |
+
def home():
|
| 11 |
+
return render_template('index.html')
|
| 12 |
+
|
| 13 |
+
@app.route('/predict',methods=['POST'])
|
| 14 |
+
def predict():
|
| 15 |
+
|
| 16 |
+
int_features = str(request.form['Input_text'])
|
| 17 |
+
beam_settings = TTSettings(num_beams=5, min_length=1, max_length=20)
|
| 18 |
+
|
| 19 |
+
model = pickle.load(open('model.pkl', 'rb'))
|
| 20 |
+
output = model.generate_text(int_features, args=beam_settings)
|
| 21 |
+
|
| 22 |
+
return render_template('index.html', prediction_text='{}'.format(output.text))
|
| 23 |
+
|
| 24 |
+
if __name__ == "__main__":
|
| 25 |
+
app.run(host='0.0.0.0', debug=True)
|
dataset_preprocess.py
ADDED
|
@@ -0,0 +1,68 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import csv
|
| 3 |
+
from datasets import load_dataset
|
| 4 |
+
|
| 5 |
+
REPLACEMENTS = [
|
| 6 |
+
(" .", "."),
|
| 7 |
+
(" ,", ","),
|
| 8 |
+
(" '", "'"),
|
| 9 |
+
(" ?", "?"),
|
| 10 |
+
(" !", "!"),
|
| 11 |
+
(" :", ":"),
|
| 12 |
+
(" ;", ";"),
|
| 13 |
+
(" n't", "n't"),
|
| 14 |
+
("2 0 0 6", "2006"),
|
| 15 |
+
("5 5", "55"),
|
| 16 |
+
("4 0 0", "400"),
|
| 17 |
+
("1 7-5 0", "1750"),
|
| 18 |
+
("2 0 %", "20%"),
|
| 19 |
+
("5 0", "50"),
|
| 20 |
+
("1 2", "12"),
|
| 21 |
+
("1 0", "10"),
|
| 22 |
+
('" ballast water', '"ballast water')
|
| 23 |
+
]
|
| 24 |
+
|
| 25 |
+
def remove_excess_spaces(text):
|
| 26 |
+
for old, new in REPLACEMENTS:
|
| 27 |
+
text = text.replace(old, new)
|
| 28 |
+
return text
|
| 29 |
+
|
| 30 |
+
def generate_csv(csv_path, dataset):
|
| 31 |
+
os.makedirs(os.path.dirname(csv_path), exist_ok=True)
|
| 32 |
+
with open(csv_path, 'w', newline='', encoding='utf-8') as csvfile:
|
| 33 |
+
writer = csv.writer(csvfile)
|
| 34 |
+
writer.writerow(["input", "target"])
|
| 35 |
+
for case in dataset:
|
| 36 |
+
input_text = "grammar: " + case["sentence"]
|
| 37 |
+
input_text = remove_excess_spaces(input_text)
|
| 38 |
+
for correction in case["corrections"]:
|
| 39 |
+
correction = remove_excess_spaces(correction)
|
| 40 |
+
if input_text and correction:
|
| 41 |
+
writer.writerow([input_text, correction])
|
| 42 |
+
|
| 43 |
+
train_dataset = load_dataset("jfleg", split="validation[:]")
|
| 44 |
+
eval_dataset = load_dataset("jfleg", split="test[:]")
|
| 45 |
+
|
| 46 |
+
generate_csv("Dataset/JFLEG/train.csv", train_dataset)
|
| 47 |
+
generate_csv("Dataset/JFLEG/eval.csv", eval_dataset)
|
| 48 |
+
|
| 49 |
+
c4_dataset = load_dataset("liweili/c4_200m", split="train", streaming=True)
|
| 50 |
+
|
| 51 |
+
def c4_generate_csv(csv_path, iterator, num_examples):
|
| 52 |
+
os.makedirs(os.path.dirname(csv_path), exist_ok=True)
|
| 53 |
+
with open(csv_path, 'w', newline='', encoding='utf-8') as csvfile:
|
| 54 |
+
writer = csv.writer(csvfile)
|
| 55 |
+
writer.writerow(["input", "target"])
|
| 56 |
+
for _ in range(num_examples):
|
| 57 |
+
try:
|
| 58 |
+
data = next(iterator)
|
| 59 |
+
input_text = "grammar: " + data["input"]
|
| 60 |
+
input_text = remove_excess_spaces(input_text)
|
| 61 |
+
correction = remove_excess_spaces(data["output"])
|
| 62 |
+
if input_text and correction:
|
| 63 |
+
writer.writerow([input_text, correction])
|
| 64 |
+
except StopIteration:
|
| 65 |
+
break
|
| 66 |
+
|
| 67 |
+
c4_iterator = iter(c4_dataset)
|
| 68 |
+
c4_generate_csv("Dataset/C4_200M/c4data.csv", c4_iterator, num_examples=3500)
|
model.py
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import pickle
|
| 2 |
+
from happytransformer import HappyTextToText, TTTrainArgs
|
| 3 |
+
|
| 4 |
+
happy_tt = HappyTextToText("T5", "t5-base")
|
| 5 |
+
|
| 6 |
+
train_args = TTTrainArgs(
|
| 7 |
+
batch_size=8,
|
| 8 |
+
num_train_epochs=5,
|
| 9 |
+
max_input_length=512,
|
| 10 |
+
max_output_length=150
|
| 11 |
+
)
|
| 12 |
+
happy_tt.train("Dataset/train.csv", args=train_args)
|
| 13 |
+
|
| 14 |
+
eval_args = TTTrainArgs(
|
| 15 |
+
batch_size=8,
|
| 16 |
+
max_input_length=512,
|
| 17 |
+
max_output_length=150
|
| 18 |
+
)
|
| 19 |
+
|
| 20 |
+
happy_tt.eval("Dataset/eval.csv", args=eval_args)
|
| 21 |
+
|
| 22 |
+
pickle.dump(happy_tt, open('model.pkl','wb'))
|
requirements.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Flask==3.1.0
|
| 2 |
+
happytransformer==3.0.0
|
| 3 |
+
numpy== 2.2.0
|
| 4 |
+
st-annotated-text
|
| 5 |
+
bokeh
|
| 6 |
+
streamlit-bokeh-events
|
| 7 |
+
matplotlib
|
static/style.css
ADDED
|
@@ -0,0 +1,120 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
@import url("https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;500&display=swap");
|
| 2 |
+
|
| 3 |
+
body {
|
| 4 |
+
margin: 0;
|
| 5 |
+
padding: 0;
|
| 6 |
+
font-family: "Roboto", sans-serif;
|
| 7 |
+
background: #f9fafb;
|
| 8 |
+
display: flex;
|
| 9 |
+
justify-content: center;
|
| 10 |
+
align-items: center;
|
| 11 |
+
min-height: 100vh;
|
| 12 |
+
color: #2d3748;
|
| 13 |
+
}
|
| 14 |
+
|
| 15 |
+
.container {
|
| 16 |
+
width: 90%;
|
| 17 |
+
max-width: 500px;
|
| 18 |
+
padding: 20px;
|
| 19 |
+
background: #ffffff;
|
| 20 |
+
border-radius: 10px;
|
| 21 |
+
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1);
|
| 22 |
+
}
|
| 23 |
+
|
| 24 |
+
.header {
|
| 25 |
+
text-align: center;
|
| 26 |
+
margin-bottom: 20px;
|
| 27 |
+
}
|
| 28 |
+
|
| 29 |
+
.header h2 {
|
| 30 |
+
font-size: 1.6rem;
|
| 31 |
+
color: #1a202c;
|
| 32 |
+
margin: 0;
|
| 33 |
+
}
|
| 34 |
+
|
| 35 |
+
.header p {
|
| 36 |
+
font-size: 0.9rem;
|
| 37 |
+
color: #718096;
|
| 38 |
+
margin: 5px 0 0;
|
| 39 |
+
}
|
| 40 |
+
|
| 41 |
+
.form-wrapper {
|
| 42 |
+
text-align: center;
|
| 43 |
+
}
|
| 44 |
+
|
| 45 |
+
h1 {
|
| 46 |
+
font-size: 1.4rem;
|
| 47 |
+
font-weight: 500;
|
| 48 |
+
margin-bottom: 20px;
|
| 49 |
+
color: #1a202c;
|
| 50 |
+
}
|
| 51 |
+
|
| 52 |
+
textarea {
|
| 53 |
+
width: 90%;
|
| 54 |
+
height: 140px;
|
| 55 |
+
padding: 15px;
|
| 56 |
+
border: 1px solid #e2e8f0;
|
| 57 |
+
border-radius: 8px;
|
| 58 |
+
resize: none;
|
| 59 |
+
font-size: 16px;
|
| 60 |
+
color: #4a5568;
|
| 61 |
+
background: #f7fafc;
|
| 62 |
+
transition: border-color 0.3s, box-shadow 0.3s;
|
| 63 |
+
}
|
| 64 |
+
|
| 65 |
+
textarea:focus {
|
| 66 |
+
outline: none;
|
| 67 |
+
border-color: #4299e1;
|
| 68 |
+
box-shadow: 0 0 0 4px rgba(66, 153, 225, 0.3);
|
| 69 |
+
}
|
| 70 |
+
|
| 71 |
+
.btn {
|
| 72 |
+
display: inline-block;
|
| 73 |
+
width: 100%;
|
| 74 |
+
padding: 12px 20px;
|
| 75 |
+
background: #4299e1;
|
| 76 |
+
color: #ffffff;
|
| 77 |
+
font-size: 16px;
|
| 78 |
+
font-weight: 500;
|
| 79 |
+
text-transform: uppercase;
|
| 80 |
+
border: none;
|
| 81 |
+
border-radius: 8px;
|
| 82 |
+
cursor: pointer;
|
| 83 |
+
transition: background 0.3s ease;
|
| 84 |
+
margin-top: 15px;
|
| 85 |
+
}
|
| 86 |
+
|
| 87 |
+
.btn:hover {
|
| 88 |
+
background: #2b6cb0;
|
| 89 |
+
}
|
| 90 |
+
|
| 91 |
+
.prediction {
|
| 92 |
+
margin-top: 20px;
|
| 93 |
+
padding: 15px;
|
| 94 |
+
font-size: 16px;
|
| 95 |
+
background: #edf2f7;
|
| 96 |
+
border-radius: 8px;
|
| 97 |
+
border: 1px solid #e2e8f0;
|
| 98 |
+
color: #2d3748;
|
| 99 |
+
text-align: left;
|
| 100 |
+
}
|
| 101 |
+
|
| 102 |
+
.footer {
|
| 103 |
+
margin-top: 20px;
|
| 104 |
+
text-align: center;
|
| 105 |
+
}
|
| 106 |
+
|
| 107 |
+
.footer a {
|
| 108 |
+
text-decoration: none;
|
| 109 |
+
font-size: 14px;
|
| 110 |
+
color: #2b6cb0;
|
| 111 |
+
display: flex;
|
| 112 |
+
justify-content: center;
|
| 113 |
+
align-items: center;
|
| 114 |
+
gap: 8px;
|
| 115 |
+
}
|
| 116 |
+
|
| 117 |
+
.footer a img {
|
| 118 |
+
width: 20px;
|
| 119 |
+
height: 20px;
|
| 120 |
+
}
|
streamlit_app.py
ADDED
|
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import streamlit as st
|
| 2 |
+
from happytransformer import HappyTextToText, TTSettings
|
| 3 |
+
|
| 4 |
+
st.set_page_config(page_title="Grammar Correction Tool", layout="centered")
|
| 5 |
+
|
| 6 |
+
checkpoint = "team-writing-assistant/t5-base-c4jfleg"
|
| 7 |
+
|
| 8 |
+
@st.cache_resource
|
| 9 |
+
def get_happy_text(model_name):
|
| 10 |
+
return HappyTextToText("T5", model_name)
|
| 11 |
+
|
| 12 |
+
happy_tt = get_happy_text(checkpoint)
|
| 13 |
+
args = TTSettings(num_beams=5, min_length=1)
|
| 14 |
+
|
| 15 |
+
st.title(" NLP - Grammar Correction Tool")
|
| 16 |
+
st.subheader("Software - Group 2")
|
| 17 |
+
st.markdown("""
|
| 18 |
+
Simply enter your text below or use one of the example sentences to get started!
|
| 19 |
+
""")
|
| 20 |
+
|
| 21 |
+
col1, col2 = st.columns(2)
|
| 22 |
+
|
| 23 |
+
with col1:
|
| 24 |
+
if st.button("Example 1: Incorrect Grammar"):
|
| 25 |
+
st.session_state.input_text = "Speed of light is fastest then speed of sound"
|
| 26 |
+
with col2:
|
| 27 |
+
if st.button("Example 2: Common Mistake"):
|
| 28 |
+
st.session_state.input_text = "Who are the president?"
|
| 29 |
+
|
| 30 |
+
input_text = st.text_area("Enter your text here:", st.session_state.get("input_text", ""))
|
| 31 |
+
|
| 32 |
+
if st.button("Correct Grammar"):
|
| 33 |
+
if input_text.strip():
|
| 34 |
+
with st.spinner("Correcting grammar..."):
|
| 35 |
+
formatted_input = "grammar: " + input_text
|
| 36 |
+
result = happy_tt.generate_text(formatted_input, args=args)
|
| 37 |
+
st.markdown("### Corrected Text:")
|
| 38 |
+
st.write(result.text.strip())
|
| 39 |
+
else:
|
| 40 |
+
st.warning("Please enter text to correct.")
|
| 41 |
+
|
| 42 |
+
st.markdown("---")
|
templates/index.html
ADDED
|
@@ -0,0 +1,38 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
<!DOCTYPE html>
|
| 2 |
+
<html lang="en">
|
| 3 |
+
<head>
|
| 4 |
+
<meta charset="UTF-8" />
|
| 5 |
+
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
| 6 |
+
<title>NLP - Grammar Correction</title>
|
| 7 |
+
<link
|
| 8 |
+
href="https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;500&display=swap"
|
| 9 |
+
rel="stylesheet"
|
| 10 |
+
/>
|
| 11 |
+
<link
|
| 12 |
+
rel="stylesheet"
|
| 13 |
+
href="{{ url_for('static', filename='style.css') }}"
|
| 14 |
+
/>
|
| 15 |
+
</head>
|
| 16 |
+
<body>
|
| 17 |
+
<div class="container">
|
| 18 |
+
<div class="header">
|
| 19 |
+
<h2>NLP - Grammar Correction</h2>
|
| 20 |
+
<p>Software - Group 2</p>
|
| 21 |
+
</div>
|
| 22 |
+
<div class="form-wrapper">
|
| 23 |
+
<form action="{{ url_for('predict') }}" method="post">
|
| 24 |
+
<textarea
|
| 25 |
+
name="Input_text"
|
| 26 |
+
placeholder="Enter a sentence or paragraph to correct grammar..."
|
| 27 |
+
required="required"
|
| 28 |
+
></textarea>
|
| 29 |
+
<button type="submit" class="btn">Correct</button>
|
| 30 |
+
</form>
|
| 31 |
+
{% if prediction_text %}
|
| 32 |
+
<div class="prediction">{{ prediction_text }}</div>
|
| 33 |
+
{% endif %}
|
| 34 |
+
</div>
|
| 35 |
+
<div class="footer"></div>
|
| 36 |
+
</div>
|
| 37 |
+
</body>
|
| 38 |
+
</html>
|