Update app.py

app.py

@@ -1,5 +1,5 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
+import os
+import gradio as gr
 from transformers import pipeline
 import spacy
 import subprocess
@@ -7,9 +7,6 @@ import nltk
 from nltk.corpus import wordnet
 from spellchecker import SpellChecker
 
-# Initialize FastAPI app
-app = FastAPI()
-
 # Initialize the English text classification pipeline for AI detection
 pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
 
@@ -27,38 +24,152 @@ except OSError:
     subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
     nlp = spacy.load("en_core_web_sm")
 
-# Request body models
-class TextRequest(BaseModel):
-    text: str
-
-class TextResponse(BaseModel):
-    result: str
-
 # Function to predict the label and score for English text (AI Detection)
-def predict_en(text: str):
+def predict_en(text):
     res = pipeline_en(text)[0]
-    return {"label": res['label'], "score": res['score']}
+    return res['label'], res['score']
 
 # Function to get synonyms using NLTK WordNet
-def get_synonyms_nltk(word: str, pos: str):
-    pos_tag = None
-    if pos == "VERB":
-        pos_tag = wordnet.VERB
-    elif pos == "NOUN":
-        pos_tag = wordnet.NOUN
-    elif pos == "ADJ":
-        pos_tag = wordnet.ADJ
-    elif pos == "ADV":
-        pos_tag = wordnet.ADV
-    
-    synsets = wordnet.synsets(word, pos=pos_tag)
+def get_synonyms_nltk(word, pos):
+    synsets = wordnet.synsets(word, pos=pos)
     if synsets:
         lemmas = synsets[0].lemmas()
         return [lemma.name() for lemma in lemmas]
     return []
 
+# Function to remove redundant and meaningless words
+def remove_redundant_words(text):
+    doc = nlp(text)
+    meaningless_words = {"actually", "basically", "literally", "really", "very", "just"}
+    filtered_text = [token.text for token in doc if token.text.lower() not in meaningless_words]
+    return ' '.join(filtered_text)
+
+# Function to capitalize the first letter of sentences and proper nouns
+def capitalize_sentences_and_nouns(text):
+    doc = nlp(text)
+    corrected_text = []
+
+    for sent in doc.sents:
+        sentence = []
+        for token in sent:
+            if token.i == sent.start:  # First word of the sentence
+                sentence.append(token.text.capitalize())
+            elif token.pos_ == "PROPN":  # Proper noun
+                sentence.append(token.text.capitalize())
+            else:
+                sentence.append(token.text)
+        corrected_text.append(' '.join(sentence))
+
+    return '\n'.join(corrected_text)  # Preserve paragraphs by joining sentences with newline
+
+# Function to force capitalization of the first letter of every sentence
+def force_first_letter_capital(text):
+    sentences = text.split(". ")  # Split by period to get each sentence
+    capitalized_sentences = [sentence[0].capitalize() + sentence[1:] if sentence else "" for sentence in sentences]
+    return ". ".join(capitalized_sentences)
+
+# Function to correct tense errors in a sentence
+def correct_tense_errors(text):
+    doc = nlp(text)
+    corrected_text = []
+    for token in doc:
+        if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}:
+            lemma = wordnet.morphy(token.text, wordnet.VERB) or token.text
+            corrected_text.append(lemma)
+        else:
+            corrected_text.append(token.text)
+    return ' '.join(corrected_text)
+
+# Function to correct singular/plural errors
+def correct_singular_plural_errors(text):
+    doc = nlp(text)
+    corrected_text = []
+    
+    for token in doc:
+        if token.pos_ == "NOUN":
+            if token.tag_ == "NN":  # Singular noun
+                if any(child.text.lower() in ['many', 'several', 'few'] for child in token.head.children):
+                    corrected_text.append(token.lemma_ + 's')
+                else:
+                    corrected_text.append(token.text)
+            elif token.tag_ == "NNS":  # Plural noun
+                if any(child.text.lower() in ['a', 'one'] for child in token.head.children):
+                    corrected_text.append(token.lemma_)
+                else:
+                    corrected_text.append(token.text)
+        else:
+            corrected_text.append(token.text)
+    
+    return ' '.join(corrected_text)
+
+# Function to check and correct article errors
+def correct_article_errors(text):
+    doc = nlp(text)
+    corrected_text = []
+    for token in doc:
+        if token.text in ['a', 'an']:
+            next_token = token.nbor(1)
+            if token.text == "a" and next_token.text[0].lower() in "aeiou":
+                corrected_text.append("an")
+            elif token.text == "an" and next_token.text[0].lower() not in "aeiou":
+                corrected_text.append("a")
+            else:
+                corrected_text.append(token.text)
+        else:
+            corrected_text.append(token.text)
+    return ' '.join(corrected_text)
+
+# Function to get the correct synonym while maintaining verb form
+def replace_with_synonym(token):
+    pos = None
+    if token.pos_ == "VERB":
+        pos = wordnet.VERB
+    elif token.pos_ == "NOUN":
+        pos = wordnet.NOUN
+    elif token.pos_ == "ADJ":
+        pos = wordnet.ADJ
+    elif token.pos_ == "ADV":
+        pos = wordnet.ADV
+    
+    synonyms = get_synonyms_nltk(token.lemma_, pos)
+    
+    if synonyms:
+        synonym = synonyms[0]
+        if token.tag_ == "VBG":  # Present participle (e.g., running)
+            synonym = synonym + 'ing'
+        elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
+            synonym = synonym + 'ed'
+        elif token.tag_ == "VBZ":  # Third-person singular present
+            synonym = synonym + 's'
+        return synonym
+    return token.text
+
+# Function to check for and avoid double negatives
+def correct_double_negatives(text):
+    doc = nlp(text)
+    corrected_text = []
+    for token in doc:
+        if token.text.lower() == "not" and any(child.text.lower() == "never" for child in token.head.children):
+            corrected_text.append("always")
+        else:
+            corrected_text.append(token.text)
+    return ' '.join(corrected_text)
+
+# Function to ensure subject-verb agreement
+def ensure_subject_verb_agreement(text):
+    doc = nlp(text)
+    corrected_text = []
+    for token in doc:
+        if token.dep_ == "nsubj" and token.head.pos_ == "VERB":
+            if token.tag_ == "NN" and token.head.tag_ != "VBZ":  # Singular noun, should use singular verb
+                corrected_text.append(token.head.lemma_ + "s")
+            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":  # Plural noun, should not use singular verb
+                corrected_text.append(token.head.lemma_)
+        corrected_text.append(token.text)
+    return ' '.join(corrected_text)
+
 # Function to correct spelling errors
-def correct_spelling(text: str):
+def correct_spelling(text):
     words = text.split()
     corrected_words = []
     for word in words:
@@ -67,20 +178,20 @@ def correct_spelling(text: str):
     return ' '.join(corrected_words)
 
 # Function to rephrase text and replace words with their synonyms while maintaining form
-def rephrase_with_synonyms(text: str):
+def rephrase_with_synonyms(text):
     doc = nlp(text)
     rephrased_text = []
 
     for token in doc:
         pos_tag = None
         if token.pos_ == "NOUN":
-            pos_tag = "NOUN"
+            pos_tag = wordnet.NOUN
         elif token.pos_ == "VERB":
-            pos_tag = "VERB"
+            pos_tag = wordnet.VERB
         elif token.pos_ == "ADJ":
-            pos_tag = "ADJ"
+            pos_tag = wordnet.ADJ
         elif token.pos_ == "ADV":
-            pos_tag = "ADV"
+            pos_tag = wordnet.ADV
         
         if pos_tag:
             synonyms = get_synonyms_nltk(token.text, pos_tag)
@@ -103,21 +214,49 @@ def rephrase_with_synonyms(text: str):
 
     return ' '.join(rephrased_text)
 
-# FastAPI endpoints
-@app.post("/predict/")
-def predict(text_request: TextRequest):
-    return predict_en(text_request.text)
+# Function to paraphrase and correct grammar with enhanced accuracy
+def paraphrase_and_correct(text):
+    # Remove meaningless or redundant words first
+    cleaned_text = remove_redundant_words(text)
+    
+    # Capitalize sentences and nouns
+    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
+    
+    # Ensure first letter of each sentence is capitalized
+    paraphrased_text = force_first_letter_capital(paraphrased_text)
+    
+    # Apply grammatical corrections
+    paraphrased_text = correct_article_errors(paraphrased_text)
+    paraphrased_text = correct_singular_plural_errors(paraphrased_text)
+    paraphrased_text = correct_tense_errors(paraphrased_text)
+    paraphrased_text = correct_double_negatives(paraphrased_text)
+    paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
+    
+    # Rephrase with synonyms while maintaining grammatical forms
+    paraphrased_text = rephrase_with_synonyms(paraphrased_text)
+    
+    # Correct spelling errors
+    paraphrased_text = correct_spelling(paraphrased_text)
+    
+    return paraphrased_text
+
+# Gradio app setup with two tabs
+with gr.Blocks() as demo:
+    with gr.Tab("AI Detection"):
+        t1 = gr.Textbox(lines=5, label='Text')
+        button1 = gr.Button("🤖 Predict!")
+        label1 = gr.Textbox(lines=1, label='Predicted Label 🎃')
+        score1 = gr.Textbox(lines=1, label='Prob')
 
-@app.post("/rephrase/")
-def rephrase(text_request: TextRequest):
-    return {"result": rephrase_with_synonyms(text_request.text)}
+        # Connect the prediction function to the button
+        button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
 
-@app.post("/correct-spelling/")
-def correct_spell(text_request: TextRequest):
-    return {"result": correct_spelling(text_request.text)}
+    with gr.Tab("Paraphrasing & Grammar Correction"):
+        t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction')
+        button2 = gr.Button("🔄 Paraphrase and Correct")
+        result2 = gr.Textbox(lines=10, label='Corrected Text', placeholder="The corrected text will appear here...")
 
-# Additional endpoints for other functionalities can be added similarly
+        # Connect the paraphrasing and correction function to the button
+        button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
 
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="127.0.0.1", port=8000)
+demo.launch(share=True)  # Share=True to create a public link