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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
st.set_page_config(page_title="Kiswahili Grammar Tutor", page_icon="🧠")
# Load model and tokenizer from Hugging Face Hub
@st.cache_resource
def load_model():
tokenizer = AutoTokenizer.from_pretrained("Bur3hani/kiswmod")
model = AutoModelForSeq2SeqLM.from_pretrained("Bur3hani/kiswmod")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
return tokenizer, model, device
tokenizer, model, device = load_model()
st.title("🧠 Kiswahili Grammar Tutor")
st.markdown("An AI assistant that acts like a Kiswahili teacher or therapist. It corrects sentences and explains grammar in a conversational way (max 3 turns).")
if "history" not in st.session_state:
st.session_state.history = []
user_input = st.text_input("Mwanafunzi:", "")
if st.button("Submit") and user_input:
# Build dialogue history
dialogue = ""
for student, teacher in st.session_state.history:
dialogue += f"Mwanafunzi: {student}\nMwalimu: {teacher}\n"
dialogue += f"Mwanafunzi: {user_input}\nMwalimu:"
# Generate response
inputs = tokenizer(dialogue, return_tensors="pt", truncation=True, padding="max_length", max_length=256).to(device)
output_ids = model.generate(**inputs, max_length=128)
reply = tokenizer.decode(output_ids[0], skip_special_tokens=True)
st.session_state.history.append((user_input, reply))
if len(st.session_state.history) > 3:
st.session_state.history = st.session_state.history[-3:]
# Display conversation
for student, teacher in st.session_state.history:
st.markdown(f"**Mwanafunzi:** {student}")
st.markdown(f"**Mwalimu:** {teacher}")
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