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import streamlit as st |
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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer, pipeline |
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from diffusers import StableDiffusionPipeline |
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import torch |
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@st.cache_resource |
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def load_models(): |
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model_name = "alirezamsh/small100" |
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tokenizer = M2M100Tokenizer.from_pretrained(model_name) |
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model = M2M100ForConditionalGeneration.from_pretrained(model_name) |
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text_generator = pipeline("text-generation", model="gpt2-medium") |
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image_pipe = StableDiffusionPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float32 |
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).to("cpu") |
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return tokenizer, model, text_generator, image_pipe |
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def translate_tamil_to_english(text, tokenizer, model): |
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tokenizer.src_lang = "ta" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True) |
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generated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.get_lang_id("en")) |
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return tokenizer.decode(generated_tokens[0], skip_special_tokens=True) |
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def generate_creative_text(prompt, generator): |
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response = generator(f"Describe creatively: {prompt}", max_length=60, num_return_sequences=1) |
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return response[0]["generated_text"] |
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def main(): |
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st.set_page_config(page_title="Tamil to English → Image & Text", layout="centered") |
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st.title("🪔 தமிழ் உரையை படமாக மாற்றவும்") |
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user_input = st.text_area("தமிழ் உரை உள்ளிடவும்:", height=150) |
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if st.button("உருவாக்கவும்"): |
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if not user_input.strip(): |
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st.warning("தயவுசெய்து ஒரு உரை உள்ளிடவும்.") |
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return |
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with st.spinner("மொழிபெயர்ப்பு நடக்கிறது..."): |
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tokenizer, model, textgen, pipe = load_models() |
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english_text = translate_tamil_to_english(user_input, tokenizer, model) |
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st.success(f"Translated to English: {english_text}") |
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with st.spinner("சிறந்த உரையை உருவாக்குகிறது..."): |
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creative = generate_creative_text(english_text, textgen) |
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st.info("✍️ Creative Text:") |
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st.write(creative) |
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with st.spinner("படம் உருவாக்கப்படுகிறது..."): |
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image = pipe(english_text, num_inference_steps=25).images[0] |
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st.image(image, caption="🎨 Generated Image", use_column_width=True) |
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if __name__ == "__main__": |
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main() |
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