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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import os
token = os.getenv("hf_token")
@st.cache_resource
def load_model():
model_name = "robzchhangte/bloomz-dv5-with-mztok"
# model_name = "robzchhangte/10-vanillagpt2-ft-INS-dv5"
tokenizer = AutoTokenizer.from_pretrained("robzchhangte/bloomz-dv5-with-mztok", token=token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=token)
return tokenizer, model
tokenizer, model = load_model()
st.title("📝 Mizo Text Generator")
prompt = st.text_area("Enter your prompt (in Mizo):", height=150)
st.text("Example: Lirthei pung nasa chu hmasawnna rah a nih rualin harsatna tam tak..")
generate_button = st.button("Generate Text")
if generate_button and prompt:
with st.spinner("Generating text..."):
inputs = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(
inputs,
max_length=50,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.subheader("Generated Text:")
st.write(generated_text)
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