Update app.py

app.py

@@ -7,39 +7,16 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
 
-# Load explanation model
-from transformers import T5ForConditionalGeneration, T5Tokenizer
+st.title("✍️ English Grammar & Writing Assistant")
 
-explain_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
-explain_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
+text = st.text_area("Enter your sentence or paragraph:")
 
-def correct_text(text):
-    input_text = "gec: " + text
-    result = pipe(input_text, max_length=512, clean_up_tokenization_spaces=True)
-    return result[0]['generated_text']
-
-def explain_corrections(original, corrected):
-    prompt = f"""Original: {original}
-Corrected: {corrected}
-Explain the changes made, identify grammar or spelling issues, and give writing improvement tips."""
-    inputs = explain_tokenizer(prompt, return_tensors="pt", truncation=True)
-    outputs = explain_model.generate(**inputs, max_length=512)
-    return explain_tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Streamlit UI
-st.title("✍️ English Writing Assistant")
-user_input = st.text_area("Enter your sentence, paragraph, or essay:")
-
-if st.button("Check & Improve"):
-    if user_input.strip() == "":
+if st.button("Correct Text"):
+    if not text.strip():
         st.warning("Please enter some text.")
     else:
-        corrected = correct_text(user_input)
-        explanation = explain_corrections(user_input, corrected)
-
+        input_text = "gec: " + text
+        result = pipe(input_text, max_length=512)
+        corrected = result[0]['generated_text']
         st.subheader("✅ Corrected Text:")
         st.write(corrected)
-
-        st.subheader("📘 Explanation & Suggestions:")
-        st.write(explanation)
-