Update app.py

app.py

@@ -6,6 +6,12 @@ import subprocess
 import nltk
 from nltk.corpus import wordnet
 from spellchecker import SpellChecker
+from fastapi import FastAPI
+from pydantic import BaseModel
+import uvicorn
+
+# Initialize FastAPI app
+api_app = FastAPI()
 
 # Initialize the English text classification pipeline for AI detection
 pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
@@ -24,19 +30,15 @@ except OSError:
     subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
     nlp = spacy.load("en_core_web_sm")
 
+# Define request models for FastAPI
+class TextRequest(BaseModel):
+    text: str
+
 # Function to predict the label and score for English text (AI Detection)
 def predict_en(text):
     res = pipeline_en(text)[0]
     return res['label'], res['score']
 
-# Function to get synonyms using NLTK WordNet
-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)
@@ -102,72 +104,6 @@ def correct_singular_plural_errors(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):
     words = text.split()
@@ -194,18 +130,16 @@ def rephrase_with_synonyms(text):
             pos_tag = wordnet.ADV
         
         if pos_tag:
-            synonyms = get_synonyms_nltk(token.text, pos_tag)
+            synonyms = wordnet.synsets(token.lemma_, pos=pos_tag)
             if synonyms:
-                synonym = synonyms[0]  # Just using the first synonym for simplicity
+                synonym = synonyms[0].lemmas()[0].name()
                 if token.pos_ == "VERB":
-                    if token.tag_ == "VBG":  # Present participle (e.g., running)
+                    if token.tag_ == "VBG":  # Present participle
                         synonym = synonym + 'ing'
-                    elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
+                    elif token.tag_ in {"VBD", "VBN"}:  # Past tense or past participle
                         synonym = synonym + 'ed'
                     elif token.tag_ == "VBZ":  # Third-person singular present
                         synonym = synonym + 's'
-                elif token.pos_ == "NOUN" and token.tag_ == "NNS":  # Plural nouns
-                    synonym += 's' if not synonym.endswith('s') else ""
                 rephrased_text.append(synonym)
             else:
                 rephrased_text.append(token.text)
@@ -226,11 +160,8 @@ def paraphrase_and_correct(text):
     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)
@@ -240,6 +171,18 @@ def paraphrase_and_correct(text):
     
     return paraphrased_text
 
+# FastAPI Endpoint for AI detection
+@api_app.post("/ai-detection")
+async def ai_detection(request: TextRequest):
+    label, score = predict_en(request.text)
+    return {"label": label, "score": score}
+
+# FastAPI Endpoint for paraphrasing and grammar correction
+@api_app.post("/paraphrase")
+async def paraphrase(request: TextRequest):
+    corrected_text = paraphrase_and_correct(request.text)
+    return {"corrected_text": corrected_text}
+
 # Gradio app setup with two tabs
 with gr.Blocks() as demo:
     with gr.Tab("AI Detection"):
@@ -259,4 +202,9 @@ with gr.Blocks() as demo:
         # Connect the paraphrasing and correction function to the button
         button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
 
-demo.launch(share=True)  # Share=True to create a public link
+# Launch the Gradio app
+demo.launch(share=True)
+
+# Run the FastAPI app in a separate thread if needed
+if __name__ == "__main__":
+    uvicorn.run(api_app, host="0.0.0.0", port=8000)