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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer |
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from transformers import MBartForConditionalGeneration, MBart50Tokenizer |
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import gradio as gr |
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import requests |
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import io |
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from PIL import Image |
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import os |
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model_name = "facebook/mbart-large-50-many-to-one-mmt" |
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tokenizer = MBart50Tokenizer.from_pretrained(model_name) |
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model = MBartForConditionalGeneration.from_pretrained(model_name) |
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API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image" |
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headers = {"Authorization": f"Bearer {os.getenv('HF_API_KEY')}"} |
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def translate_and_generate_image(tamil_text): |
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tokenizer.src_lang = "ta_IN" |
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inputs = tokenizer(tamil_text, return_tensors="pt") |
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translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"]) |
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translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0] |
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def query(payload): |
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response = requests.post(API_URL, headers=headers, json=payload) |
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return response.content |
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image_bytes = query({"inputs": translated_text}) |
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image = Image.open(io.BytesIO(image_bytes)) |
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return translated_text, image |
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iface = gr.Interface( |
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fn=translate_and_generate_image, |
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inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text here..."), |
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outputs=[gr.Textbox(label="Translated English Text"), gr.Image(label="Generated Image")], |
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title="Tamil to English Translation and Image Generation", |
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description="Translate Tamil text to English using Facebook's mbart-large-50 model and generate an image using the translated text as the prompt.", |
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) |
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iface.launch(share=True) |
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