Update app.py

app.py

@@ -1,33 +1,23 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load pre-trained models
-sentiment_pipeline = pipeline("sentiment-analysis")           # Sentiment Analysis
-summarizer_pipeline = pipeline("summarization")               # Summarization
-translation_pipeline = pipeline("translation_en_to_ur")       # English to Urdu Translation
+# Load grammar correction pipeline using T5
+grammar_correction = pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")
 
-def nlp_tasks(text):
+# Function to correct grammar
+def correct_grammar(text):
     if not text.strip():
-        return "Please enter valid text", "", ""
-
-    # NLP Tasks
-    sentiment = sentiment_pipeline(text)[0]['label']
-    summary = summarizer_pipeline(text, max_length=60, min_length=25, do_sample=False)[0]['summary_text']
-    translation = translation_pipeline(text)[0]['translation_text']
-
-    return sentiment, summary, translation
+        return "Please enter some text."
+    corrected = grammar_correction(f"grammar: {text}", max_length=128, clean_up_tokenization_spaces=True)[0]['generated_text']
+    return corrected
 
 # Gradio Interface
 interface = gr.Interface(
-    fn=nlp_tasks,
-    inputs=gr.Textbox(lines=5, placeholder="Enter your text here...", label="Input Text"),
-    outputs=[
-        gr.Textbox(label="Sentiment"),
-        gr.Textbox(label="Summary"),
-        gr.Textbox(label="Urdu Translation")
-    ],
-    title="Beginner NLP App",
-    description="This beginner-friendly app uses Hugging Face Transformers to perform Sentiment Analysis, Summarization, and English to Urdu Translation."
+    fn=correct_grammar,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your sentence with grammar mistakes...", label="Your Text"),
+    outputs=gr.Textbox(label="Corrected Text"),
+    title="📝 AI Grammar Corrector",
+    description="This app uses a fine-tuned T5 model to correct grammar and style. Powered by 🤗 Hugging Face Transformers."
 )
 
 if __name__ == "__main__":