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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from diffusers import StableDiffusionPipeline
import torch
# Load models only once
@st.cache_resource
def load_all_models():
# Load translation model
trans_model_id = "ai4bharat/indictrans2-indic-en-dist-200M"
tokenizer = AutoTokenizer.from_pretrained(trans_model_id, trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained(trans_model_id, trust_remote_code=True)
translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
# Load image generation model (Stable Diffusion 2.1)
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 tokenizer, model, translation_pipeline, img_pipe
# Streamlit UI
def main():
st.set_page_config(page_title="Tamil to English to Image Generator", layout="centered")
st.title("📸 Tamil → English → AI Image Generator")
st.markdown("Translate Tamil text to English and generate an image from it!")
# Load models
with st.spinner("Loading models..."):
tokenizer, model, translation_pipeline, img_pipe = load_all_models()
# Input
tamil_text = st.text_area("Enter Tamil text here:", height=150)
if st.button("Generate Image"):
if tamil_text.strip() == "":
st.warning("Please enter some Tamil text.")
return
# Step 1: Translate Tamil to English
with st.spinner("Translating to English..."):
translated = translation_pipeline(tamil_text, src_lang="ta", tgt_lang="en")[0]["translation_text"]
st.success(f"🔤 English Translation: `{translated}`")
# Step 2: Generate image
with st.spinner("Generating image..."):
image = img_pipe(prompt=translated).images[0]
st.image(image, caption="Generated Image", use_column_width=True)
if __name__ == "__main__":
main()
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