Update app.py

app.py

@@ -1,15 +1,14 @@
-import os
-import gradio as gr
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 from transformers import pipeline
 import spacy
 import subprocess
 import nltk
 from nltk.corpus import wordnet
 from spellchecker import SpellChecker
-from flask import Flask, jsonify, request
 
-# Initialize Flask app
-app = Flask(__name__)
+# 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")
@@ -28,83 +27,97 @@ 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):
+def predict_en(text: str):
     res = pipeline_en(text)[0]
-    return res['label'], res['score']
-
-# Other processing functions (remove redundant words, capitalization, etc.) as previously defined
-# For brevity, I'm skipping them here since they're unchanged. Make sure to include all the defined functions from the original code.
-
-# Function to paraphrase and correct grammar with enhanced accuracy
-def paraphrase_and_correct(text):
-    cleaned_text = remove_redundant_words(text)
-    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
-    paraphrased_text = force_first_letter_capital(paraphrased_text)
-    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)
-    paraphrased_text = rephrase_with_synonyms(paraphrased_text)
-    paraphrased_text = correct_spelling(paraphrased_text)
-    
-    return paraphrased_text
-
-# API Endpoint for AI Detection
-@app.route('/api/ai-detection', methods=['POST'])
-def ai_detection():
-    data = request.get_json()
-    text = data.get('text', '')
-    
-    if text:
-        label, score = predict_en(text)
-        return jsonify({"label": label, "score": score})
-    else:
-        return jsonify({"error": "No text provided"}), 400
-
-# API Endpoint for Paraphrasing and Grammar Correction
-@app.route('/api/paraphrase-correct', methods=['POST'])
-def paraphrase_and_correct_api():
-    data = request.get_json()
-    text = data.get('text', '')
+    return {"label": res['label'], "score": 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
     
-    if text:
-        corrected_text = paraphrase_and_correct(text)
-        return jsonify({"corrected_text": corrected_text})
-    else:
-        return jsonify({"error": "No text provided"}), 400
-
-# Gradio app setup with two tabs
-def launch_gradio():
-    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')
-
-            # Connect the prediction function to the button
-            button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
-
-        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...")
-
-            # 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 Gradio interface in a separate thread
-if __name__ == '__main__':
-    # Run Flask app in one thread and Gradio in another
-    from threading import Thread
-
-    # Gradio interface
-    gradio_thread = Thread(target=launch_gradio)
-    gradio_thread.start()
-
-    # Flask API
-    app.run(debug=True, port=5000)
+    synsets = wordnet.synsets(word, pos=pos_tag)
+    if synsets:
+        lemmas = synsets[0].lemmas()
+        return [lemma.name() for lemma in lemmas]
+    return []
+
+# Function to correct spelling errors
+def correct_spelling(text: str):
+    words = text.split()
+    corrected_words = []
+    for word in words:
+        corrected_word = spell.correction(word)
+        corrected_words.append(corrected_word)
+    return ' '.join(corrected_words)
+
+# Function to rephrase text and replace words with their synonyms while maintaining form
+def rephrase_with_synonyms(text: str):
+    doc = nlp(text)
+    rephrased_text = []
+
+    for token in doc:
+        pos_tag = None
+        if token.pos_ == "NOUN":
+            pos_tag = "NOUN"
+        elif token.pos_ == "VERB":
+            pos_tag = "VERB"
+        elif token.pos_ == "ADJ":
+            pos_tag = "ADJ"
+        elif token.pos_ == "ADV":
+            pos_tag = "ADV"
+        
+        if pos_tag:
+            synonyms = get_synonyms_nltk(token.text, pos_tag)
+            if synonyms:
+                synonym = synonyms[0]  # Just using the first synonym for simplicity
+                if token.pos_ == "VERB":
+                    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'
+                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)
+        else:
+            rephrased_text.append(token.text)
+
+    return ' '.join(rephrased_text)
+
+# FastAPI endpoints
+@app.post("/predict/")
+def predict(text_request: TextRequest):
+    return predict_en(text_request.text)
+
+@app.post("/rephrase/")
+def rephrase(text_request: TextRequest):
+    return {"result": rephrase_with_synonyms(text_request.text)}
+
+@app.post("/correct-spelling/")
+def correct_spell(text_request: TextRequest):
+    return {"result": correct_spelling(text_request.text)}
+
+# Additional endpoints for other functionalities can be added similarly
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000)