app.py

import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load the tokenizer and model
MODEL_NAME = "google/flan-t5-base"
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)

# Streamlit app UI
st.title("AI English Tutor")
st.write("Ask me a question or give me a sentence, and I will help you.")

# Sidebar for user to control model generation parameters
st.sidebar.title("Model Parameters")
temperature = st.sidebar.slider("Temperature", 0.1, 1.5, 1.0, 0.1)  # Default 1.0
top_p = st.sidebar.slider("Top-p (Nucleus Sampling)", 0.0, 1.0, 0.9, 0.05)  # Default 0.9
top_k = st.sidebar.slider("Top-k", 0, 100, 50, 1)  # Default 50
do_sample = st.sidebar.checkbox("Enable Random Sampling", value=True)  # Enable sampling

# Input field for the student
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"Please explain the answer to this question in simple terms: '{student_question}'"

    # Tokenize input
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
    
    # Generate response
    outputs = model.generate(
        inputs["input_ids"],
        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
    )
    
    # Decode and display the output
    response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    st.write("Tutor's Answer:", response_text)