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import streamlit as st
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import torch
@st.cache_resource
def load_all_models():
# Load Tamil ➝ English translation model
translation_pipeline = pipeline(
"translation",
model="ai4bharat/indictrans2-indic-en-dist-200M",
tokenizer="ai4bharat/indictrans2-indic-en-dist-200M",
src_lang="ta", tgt_lang="en",
device=0 if torch.cuda.is_available() else -1,
)
# Load image generation model
img_pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
revision="fp16" if torch.cuda.is_available() else None,
)
img_pipe = img_pipe.to("cuda" if torch.cuda.is_available() else "cpu")
return translation_pipeline, img_pipe
def main():
st.set_page_config(page_title="Tamil ➝ English ➝ Image", layout="centered")
st.title("🌐 Tamil to English to Image Generator")
tamil_text = st.text_area("Enter Tamil text:", height=150)
if st.button("Generate Image"):
if not tamil_text.strip():
st.warning("Please enter Tamil text.")
return
with st.spinner("Loading models..."):
translation_pipeline, img_pipe = load_all_models()
with st.spinner("Translating Tamil to English..."):
translated = translation_pipeline(tamil_text)[0]["translation_text"]
st.success(f"🔤 Translated English: `{translated}`")
with st.spinner("Generating image..."):
image = img_pipe(prompt=translated).images[0]
st.image(image, caption="🎨 AI-generated Image", use_column_width=True)
if __name__ == "__main__":
main()
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