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import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
from diffusers import StableDiffusionPipeline
import torch
# Cache models for faster loading
@st.cache_resource
def load_all_models():
# Translation model
translation_model = AutoModelForSeq2SeqLM.from_pretrained(
"ai4bharat/indictrans2-indic-en-dist-200M", trust_remote_code=True
)
translation_tokenizer = AutoTokenizer.from_pretrained(
"ai4bharat/indictrans2-indic-en-dist-200M", trust_remote_code=True
)
translation_pipeline = pipeline(
"text2text-generation", model=translation_model, tokenizer=translation_tokenizer
)
# Image generation model (Stable Diffusion)
img_pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
)
img_pipe = img_pipe.to("cuda" if torch.cuda.is_available() else "cpu")
return translation_pipeline, img_pipe
def main():
st.title("📘 Tamil to English Translator & Image Generator")
tamil_text = st.text_area("📝 Enter Tamil text (word or sentence)", height=100)
if st.button("🔄 Translate & Generate Image"):
if not tamil_text.strip():
st.warning("Please enter some Tamil text.")
return
try:
translation_pipeline, img_pipe = load_all_models()
# Prepare translation input
formatted_input = "<2en><|ta|>" + tamil_text.strip()
translated = translation_pipeline(formatted_input, max_length=256)[0]["generated_text"]
st.success("✅ English Translation:")
st.write(translated)
with st.spinner("🖼️ Generating image..."):
image = img_pipe(translated).images[0]
st.image(image, caption="🖼️ Generated from English text")
except Exception as e:
st.error(f"❌ Error: {str(e)}")
if __name__ == "__main__":
main()
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