Update app.py

app.py

@@ -1,63 +1,56 @@
-import numpy as np
-import pandas as pd
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
-from sklearn.metrics import confusion_matrix
-import json
-import string
-import string
-import re
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-import gradio as gr 
-import joblib 
+from nltk.tokenize import sent_tokenize
+from language_tool_python import LanguageTool
+from nltk.sentiment import SentimentIntensityAnalyzer
+import gradio as gr
 import nltk
+nltk.download('vader_lexicon')
+
+# Initialize LanguageTool object outside the function
+tool = LanguageTool('en-US')
+sia = SentimentIntensityAnalyzer()
+
+def grammar_check(text):
+    matches = tool.check(text)
+    corrected_sentences = tool.correct(text)
+    return corrected_sentences
+
+def analyze_sentiment(text):
+    sentiment_scores = sia.polarity_scores(text)
+
+    # Positive sentiment: score > 0
+    # Neutral sentiment: score = 0
+    # Negative sentiment: score < 0
+    sentiment = ""
+    if sentiment_scores['compound'] > 0:
+        sentiment = "Positive"
+    elif sentiment_scores['compound'] == 0:
+        sentiment = "Neutral"
+    else:
+        sentiment = "Negative"
+
+    return sentiment
+
+def sentence_generator(text):
+    sentences = sent_tokenize(text)
+    for sentence in sentences:
+        yield sentence
+
+def CsharpGrammarly(text):
+    corrected_sentences = grammar_check(text)
+    sentiment_result = analyze_sentiment(corrected_sentences)
+    total_words = len(text.split())
+    return corrected_sentences, sentiment_result, total_words
+
+iface = gr.Interface(
+    fn=CsharpGrammarly,
+    inputs=gr.inputs.Textbox(placeholder="Enter your text here..."),
+    outputs=[
+        gr.outputs.Textbox(label="Modified Grammar"),
+        gr.outputs.Textbox(label="Sentiment Analysis"),
+        gr.outputs.Textbox(label="Total Words Count")
+    ],
+    title="CSharpGrammarly",
+    description="Correct spelling, grammar, and analyze sentiment."
+)
 
-nltk.download('stopwords')
-nltk.download('punkt')
-
-# Load the trained model
-model = joblib.load('model.bin')
-
-def remove_punctuation(text):
-    punctuation_free = "".join([i for i in text if i not in string.punctuation])
-    return punctuation_free
-    
-def vectorize_text(texts):
-    vectorizer = CountVectorizer()
-    vectorizer.fit(texts)
-    text_vectorized = vectorizer.transform(texts)
-    return text_vectorized, vectorizer
-
-def test_model(text):
-    # Convert text to lowercase
-    text = text.lower()
-
-    # Remove punctuation
-    text = remove_punctuation(text)
-
-    # Remove numbers
-    text = re.sub(r'\d+', '', text)
-
-    # Remove stopwords
-    stopwords_set = set(stopwords.words('english'))
-    tokens = word_tokenize(text)
-    filtered_text = [word for word in tokens if word not in stopwords_set]
-
-    # Join the filtered tokens back into a string
-    preprocessed_text = ' '.join(filtered_text)
-    
-    # Vectorize the preprocessed text
-    vectorize_texts = vectorize_text([preprocessed_text])
-   
-    # Make prediction on the vectorized text
-    prediction = model.predict(vectorize_texts[0])[0]
-
-    # Return the prediction
-    return prediction
-
-# Create the Gradio interface
-iface = gr.Interface(fn=test_model, inputs="text", outputs="text", title="Text Classification")
 iface.launch()