Create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+
+# Load model and tokenizer from Hugging Face
+@st.cache_resource
+def load_model():
+    model_name = "vennify/t5-base-grammar-correction"
+    tokenizer = T5Tokenizer.from_pretrained(model_name)
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Function to generate corrected sentence
+def correct_sentence(sentence):
+    input_text = "gec: " + sentence
+    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(input_ids, max_length=512, num_beams=4, early_stopping=True)
+    corrected = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return corrected
+
+# Streamlit UI
+st.title("📝 Advanced Grammar Correction Assistant")
+st.write("Enter a sentence. I'll correct it and explain the changes.")
+
+user_input = st.text_area("Your Sentence", height=150)
+
+if st.button("Correct & Explain"):
+    if user_input.strip() == "":
+        st.warning("Please enter a sentence.")
+    else:
+        corrected = correct_sentence(user_input)
+
+        st.markdown("### ✅ Correction:")
+        st.success(corrected)
+
+        st.markdown("### 🔍 Explanation:")
+        st.info(f"""
+*Original:* {user_input}  
+*Corrected:* {corrected}  
+
+Here's what changed:
+- I used an AI model trained to correct grammar and sentence structure.
+- To give detailed explanations for each mistake (like verb tense, subject-verb agreement, or punctuation), the app can be extended using another model or logic to compare the two sentences line by line.
+""")
+
+        st.caption("Model used: vennify/t5-base-grammar-correction")
+