Update app.py

app.py

@@ -1,144 +1,31 @@
 import gradio as gr
-import torch
-import spacy
-import subprocess
-import nltk
-from nltk.corpus import wordnet
-from gensim import downloader as api
-from gramformer import Gramformer
-
-# Ensure necessary NLTK data is downloaded
-nltk.download('wordnet')
-nltk.download('omw-1.4')
-
-# Ensure the spaCy model is installed
-try:
-    nlp = spacy.load("en_core_web_sm")
-except OSError:
-    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
-    nlp = spacy.load("en_core_web_sm")
-
-# Load a smaller Word2Vec model from Gensim's pre-trained models
-word_vectors = api.load("glove-wiki-gigaword-50")
-
-# Check for GPU and set the device accordingly
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load Gramformer for grammar correction (model 2 for correction)
-gf = Gramformer(models=2, use_gpu=torch.cuda.is_available())
-
-# AI detection model and tokenizer remain the same as before
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-tokenizer_ai = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-model_ai = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english").to(device)
-
-# AI detection function using DistilBERT
-def detect_ai_generated(text):
-    inputs = tokenizer_ai(text, return_tensors="pt", truncation=True, max_length=512).to(device)
-    with torch.no_grad():
-        outputs = model_ai(**inputs)
-        probabilities = torch.softmax(outputs.logits, dim=1)
-    ai_probability = probabilities[0][1].item()  # Probability of being AI-generated
-    return f"AI-Generated Content Probability: {ai_probability * 100:.2f}%"
-
-# 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 check and correct tenses and verbs using spaCy
-def check_tense_and_correct(text):
-    doc = nlp(text)
-    corrected_text = []
-    
-    for token in doc:
-        if token.pos_ == 'VERB':
-            tense = token.tag_
-            if tense == 'VBZ':  
-                corrected_text.append(token.lemma_)
-            elif tense == 'VBD':  
-                corrected_text.append(token.text)
-            else:
-                corrected_text.append(token.text)
-        else:
-            corrected_text.append(token.text)
-    
-    return ' '.join(corrected_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:
-                sentence.append(token.text.capitalize())
-            elif token.pos_ == "PROPN":
-                sentence.append(token.text.capitalize())
-            else:
-                sentence.append(token.text)
-        corrected_text.append(' '.join(sentence))
-
-    return ' '.join(corrected_text)
-
-# Paraphrasing function using spaCy and NLTK
-def paraphrase_with_spacy_nltk(text):
-    doc = nlp(text)
-    paraphrased_words = []
-    
-    for token in doc:
-        pos = None
-        if token.pos_ in {"NOUN"}:
-            pos = wordnet.NOUN
-        elif token.pos_ in {"VERB"}:
-            pos = wordnet.VERB
-        elif token.pos_ in {"ADJ"}:
-            pos = wordnet.ADJ
-        elif token.pos_ in {"ADV"}:
-            pos = wordnet.ADV
-        
-        synonyms = get_synonyms_nltk(token.text.lower(), pos) if pos else []
-        if synonyms and token.pos_ in {"NOUN", "VERB", "ADJ", "ADV"} and synonyms[0] != token.text.lower():
-            paraphrased_words.append(synonyms[0])
-        else:
-            paraphrased_words.append(token.text)
-    
-    paraphrased_sentence = ' '.join(paraphrased_words)
-    corrected_text = capitalize_sentences_and_nouns(paraphrased_sentence)
+from gramformer import Gramformer  # Assuming gramformer.py is in the same directory as this app
+
+# Initialize Gramformer
+gf = Gramformer(models=1, use_gpu=False)  # Set use_gpu=True if using a GPU-enabled space
+
+def correct_sentence(input_sentence):
+    # Use the correct method from Gramformer
+    corrected_sentences = gf.correct(input_sentence, max_candidates=1)
+    return "\n".join(corrected_sentences)
+
+# Create Gradio interface
+def gradio_interface():
+    input_text = gr.inputs.Textbox(lines=2, placeholder="Enter a sentence here...")
+    output_text = gr.outputs.Textbox()
+
+    # Gradio Interface: Takes a sentence, corrects it, and outputs the correction
+    iface = gr.Interface(
+        fn=correct_sentence,
+        inputs=input_text,
+        outputs=output_text,
+        title="Grammar Correction",
+        description="Corrects grammatical errors in the input sentence using the Gramformer model."
+    )
     
-    return corrected_text
-
-# Function to correct grammar using Gramformer
-def correct_grammar(text):
-    corrected_sentences = gf.correct(text)
-    return corrected_sentences[0] if corrected_sentences else text
-
-# Combined function: Paraphrase -> Tense Check -> Capitalization -> Grammar Correction
-def paraphrase_and_correct(text):
-    paraphrased_text = paraphrase_with_spacy_nltk(text)
-    tense_checked_text = check_tense_and_correct(paraphrased_text)
-    capitalized_text = capitalize_sentences_and_nouns(tense_checked_text)
-    final_text = correct_grammar(capitalized_text)
-    
-    return final_text
-
-# Gradio interface definition
-with gr.Blocks() as interface:
-    with gr.Row():
-        with gr.Column():
-            text_input = gr.Textbox(lines=5, label="Input Text")
-            detect_button = gr.Button("AI Detection")
-            paraphrase_button = gr.Button("Paraphrase & Correct")
-        with gr.Column():
-            output_text = gr.Textbox(label="Output")
-
-    detect_button.click(detect_ai_generated, inputs=text_input, outputs=output_text)
-    paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)
+    return iface
 
-# Launch the Gradio app
-interface.launch(debug=False)
+# Run the Gradio app
+if __name__ == "__main__":
+    iface = gradio_interface()
+    iface.launch()