Update app.py

app.py

@@ -1,7 +1,6 @@
-import streamlit as st
 from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
 
-# Load the model with the `ignore_mismatched_sizes=True` flag
+# Load the model and tokenizer with the ignore_mismatched_sizes parameter
 model = AutoModelForSeq2SeqLM.from_pretrained(
     "danrdoran/flan-t5-grammar-correction-simplified-squad",
     ignore_mismatched_sizes=True
@@ -9,9 +8,15 @@ model = AutoModelForSeq2SeqLM.from_pretrained(
 tokenizer = AutoTokenizer.from_pretrained("danrdoran/flan-t5-grammar-correction-simplified-squad")
 
 # Set up the Hugging Face pipeline for text2text-generation task
-model_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+model_pipeline = pipeline(
+    "text2text-generation", 
+    model=model, 
+    tokenizer=tokenizer
+)
 
 # Streamlit app UI
+import streamlit as st
+
 st.title("AI English Tutor")
 st.write("Ask me a question or give me a sentence, and I will help you.")
 
@@ -27,18 +32,16 @@ student_question = st.text_input("Ask your question!")
 
 # Generate and display response using the Hugging Face model
 if student_question:
-    # Adjust prompt to ask for complete sentences
     prompt = f"Answer the following question in complete sentences: '{student_question}'"
-    
+
     # Call the pipeline with adjusted parameters
     response = model_pipeline(
-        prompt, 
-        #max_length=150,  # Adjust this based on how long you'd want the responses
-        #min_length=50,   # Encourage longer responses
-        temperature=temperature,  # Control randomness
-        top_p=top_p,  # Nucleus sampling
-        top_k=top_k,  # Top-k sampling
-        do_sample=do_sample  # Enable or disable sampling
+        prompt,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        do_sample=do_sample,
+        decoder_start_token_id=tokenizer.pad_token_id  # Add the required decoder_start_token_id
     )
     
     st.write("Tutor's Answer:", response[0]['generated_text'])