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app_hf_streamlit.py

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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}")

requirements.txt

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+streamlit
+transformers
+torch