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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from diffusers import StableDiffusionPipeline
import torch
st.set_page_config(
page_title="Tamil Creative Studio",
page_icon="🇮🇳",
layout="centered",
)
def load_css():
st.markdown(
"""<style>
.header {
text-align: center;
padding: 20px;
background: #f9f9f9;
border-radius: 10px;
margin-bottom: 20px;
}
.header h1 { color: #cc0000; }
.header p { color: #333; font-style: italic; }
</style>""",
unsafe_allow_html=True,
)
@st.cache_resource(show_spinner=False)
def load_all_models():
model_id = "ai4bharat/indictrans2-indic-en-dist-200M"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
text_gen = pipeline("text-generation", model="gpt2", device=-1)
img_pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base",
torch_dtype=torch.float32,
safety_checker=None
).to("cpu")
return tokenizer, model, text_gen, img_pipe
def translate_tamil(text, tokenizer, model):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
outs = model.generate(**inputs, max_length=150, num_beams=5, early_stopping=True)
return tokenizer.decode(outs[0], skip_special_tokens=True)
def main():
load_css()
st.markdown(
'<div class="header"><h1>🌐 தமிழ் → English → Creative Studio</h1>'
'<p>Translate Tamil text and generate creative content</p></div>',
unsafe_allow_html=True
)
tokenizer, model, text_gen, img_pipe = load_all_models()
tamil_text = st.text_area("**தமிழ் உரை:**", height=150, placeholder="உங்கள் உரையை இங்கே உள்ளிடவும்...")
if st.button("உருவாக்கு"):
if not tamil_text.strip():
st.warning("உரையை உள்ளிடவும்.")
return
with st.spinner("மொழிபெயர்க்கிறது..."):
eng = translate_tamil(tamil_text, tokenizer, model)
st.success(eng)
with st.spinner("உரை உருவாக்குதல்..."):
creative = text_gen(f"Create a creative description about: {eng}", max_length=80, num_return_sequences=1)[0]["generated_text"]
st.info(creative)
with st.spinner("படத்தை உருவாக்குதல்..."):
img = img_pipe(eng, num_inference_steps=40, guidance_scale=8.5).images[0]
st.image(img, caption="Generated Image", use_column_width=True)
if __name__ == "__main__":
main()
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