Update app.py

app.py

@@ -10,8 +10,8 @@ from fastapi import FastAPI
 from pydantic import BaseModel
 import uvicorn
 
-# Initialize FastAPI app
-api_app = FastAPI()
+# Initialize FastAPI
+app = FastAPI()
 
 # Initialize the English text classification pipeline for AI detection
 pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
@@ -30,8 +30,8 @@ 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):
+# Define the input model for FastAPI (for validation)
+class TextInput(BaseModel):
     text: str
 
 # Function to predict the label and score for English text (AI Detection)
@@ -39,80 +39,6 @@ def predict_en(text):
     res = pipeline_en(text)[0]
     return res['label'], res['score']
 
-# 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 correct spelling errors
-def correct_spelling(text):
-    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):
     doc = nlp(text)
@@ -130,16 +56,9 @@ def rephrase_with_synonyms(text):
             pos_tag = wordnet.ADV
         
         if pos_tag:
-            synonyms = wordnet.synsets(token.lemma_, pos=pos_tag)
+            synonyms = get_synonyms_nltk(token.text, pos_tag)
             if synonyms:
-                synonym = synonyms[0].lemmas()[0].name()
-                if token.pos_ == "VERB":
-                    if token.tag_ == "VBG":  # Present participle
-                        synonym = synonym + 'ing'
-                    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'
+                synonym = synonyms[0]
                 rephrased_text.append(synonym)
             else:
                 rephrased_text.append(token.text)
@@ -148,63 +67,64 @@ def rephrase_with_synonyms(text):
 
     return ' '.join(rephrased_text)
 
-# Function to paraphrase and correct grammar with enhanced accuracy
+# Function to paraphrase and correct text
 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_singular_plural_errors(paraphrased_text)
-    paraphrased_text = correct_tense_errors(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
-
-# FastAPI Endpoint for AI detection
-@api_app.post("/ai-detection")
-async def ai_detection(request: TextRequest):
-    label, score = predict_en(request.text)
+    # [Place your processing logic here, such as removing redundant words, correcting grammar, etc.]
+    return rephrase_with_synonyms(text)
+
+# Define FastAPI route for AI detection
+@app.post("/ai-detect")
+async def ai_detect(input: TextInput):
+    label, score = predict_en(input.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)
+# Define FastAPI route for paraphrasing and grammar correction
+@app.post("/paraphrase")
+async def paraphrase(input: TextInput):
+    corrected_text = paraphrase_and_correct(input.text)
     return {"corrected_text": corrected_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')
-
-        # 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...")
+# 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 []
+
+# Set up Gradio UI
+def gradio_ui():
+    with gr.Blocks() as demo:
+        with gr.Tab("AI Detection"):
+            t1 = gr.Textbox(lines=5, label='Text for AI Detection')
+            button1 = gr.Button("🤖 Predict AI Detection")
+            label1 = gr.Textbox(lines=1, label='Predicted Label')
+            score1 = gr.Textbox(lines=1, label='Prediction Score')
+
+            # Connect the prediction function to the Gradio UI
+            button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
+
+        with gr.Tab("Paraphrasing & Grammar Correction"):
+            t2 = gr.Textbox(lines=5, label='Text for Paraphrasing and Grammar Correction')
+            button2 = gr.Button("🔄 Paraphrase and Correct")
+            result2 = gr.Textbox(lines=10, label='Corrected Text', placeholder="Corrected and paraphrased text will appear here")
+
+            # Connect the paraphrasing and correction function to the Gradio UI
+            button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
+
+    # Start Gradio on port 7860 and share the app publicly
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+
+# Run both FastAPI and Gradio concurrently
+if __name__ == "__main__":
+    import multiprocessing
 
-        # Connect the paraphrasing and correction function to the button
-        button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
+    # Run FastAPI server in one process
+    fastapi_process = multiprocessing.Process(target=uvicorn.run, args=(app,), kwargs={"host": "0.0.0.0", "port": 8000})
+    fastapi_process.start()
 
-# Launch the Gradio app
-demo.launch(share=True)
+    # Run Gradio interface in another process
+    gradio_ui()
 
-# Run the FastAPI app in a separate thread if needed
-if __name__ == "__main__":
-    uvicorn.run(api_app, host="0.0.0.0", port=8000)
+    # When done, stop both processes
+    fastapi_process.join()