Update app.py

app.py

@@ -1,56 +1,60 @@
 import streamlit as st
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 import torch
 
 # Load grammar correction model
 @st.cache_resource
 def load_grammar_model():
-    model_name = "prithivida/grammar_error_correcter_v1"
+    model_name = "vennify/t5-base-grammar-correction"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
     return tokenizer, model
 
 # Load explanation model
 @st.cache_resource
-def load_explainer():
-    explainer = pipeline("text2text-generation", model="google/flan-t5-base", max_length=256)
-    return explainer
+def load_explanation_model():
+    return pipeline("text2text-generation", model="google/flan-t5-large", max_length=512)
 
 grammar_tokenizer, grammar_model = load_grammar_model()
-explanation_model = load_explainer()
+explanation_model = load_explanation_model()
 
 # Grammar correction function
 def correct_grammar(text):
-    input_text = "gec: " + text
-    inputs = grammar_tokenizer.encode(input_text, return_tensors="pt", truncation=True)
+    inputs = grammar_tokenizer.encode(text, return_tensors="pt", truncation=True)
     outputs = grammar_model.generate(inputs, max_length=512, num_beams=4, early_stopping=True)
-    corrected_text = grammar_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return corrected_text
-
-# Explanation function using second model
-def explain_correction(original, corrected):
-    prompt = f"Explain the grammar improvements made from: \"{original}\" to: \"{corrected}\""
-    result = explanation_model(prompt)[0]['generated_text']
-    return result
-
-# Streamlit App UI
-st.title("📝 Smart Grammar Correction with Explanations")
-st.write("Enter your text below. The AI will correct grammar **and explain why** the changes were made using grammar principles.")
-
-user_input = st.text_area("Your Text", height=200, placeholder="Type or paste your text here...")
-
-if st.button("Correct and Explain"):
+    corrected = grammar_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return corrected
+
+# Explanation function
+def get_detailed_feedback(original, corrected):
+    prompt = (
+        f"Analyze and explain all grammar, spelling, and punctuation corrections made when changing the following sentence:\n\n"
+        f"Original: {original}\n"
+        f"Corrected: {corrected}\n\n"
+        f"Give a list of corrections with the reason for each, and also suggest how the user can improve their writing."
+    )
+    explanation = explanation_model(prompt)[0]['generated_text']
+    return explanation
+
+# Streamlit UI
+st.set_page_config(page_title="Grammar Fixer & Coach", layout="centered")
+st.title("🧠 Grammar Fixer & Writing Coach")
+st.write("Paste your sentence or paragraph. The AI will correct it and explain each fix to help you learn.")
+
+user_input = st.text_area("✍️ Enter your text below:", height=200, placeholder="e.g., I, want you! to please foucs on you work only!!")
+
+if st.button("Correct & Explain"):
     if user_input.strip():
         with st.spinner("Correcting grammar..."):
             corrected = correct_grammar(user_input)
 
         with st.spinner("Explaining corrections..."):
-            explanation = explain_correction(user_input, corrected)
+            explanation = get_detailed_feedback(user_input, corrected)
 
         st.subheader("✅ Corrected Text")
         st.success(corrected)
 
-        st.subheader("📘 Explanation (Why it was changed)")
+        st.subheader("📘 Detailed Explanation")
         st.markdown(explanation)
     else:
-        st.warning("Please enter some text to correct.")
+        st.warning("Please enter some text.")