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| import os | |
| import gradio as gr | |
| from PIL import Image | |
| from lang_list import LANGS | |
| ##Image Classification | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| extractor = AutoFeatureExtractor.from_pretrained("rajistics/finetuned-indian-food") | |
| model = AutoModelForImageClassification.from_pretrained("rajistics/finetuned-indian-food") | |
| def image_to_text(imagepic): | |
| inputs = extractor(images=imagepic, return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| predicted_class_idx = logits.argmax(-1).item() | |
| return (model.config.id2label[predicted_class_idx]) | |
| ##Translation | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline | |
| #Get list of language codes: https://github.com/facebookresearch/flores/tree/main/flores200#languages-in-flores-200 | |
| modelt = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") | |
| tokenizert = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") | |
| def translation(text,target): | |
| translator = pipeline('translation', model=modelt, tokenizer=tokenizert, src_lang="eng_Latn", tgt_lang=target) | |
| output = translator(text) | |
| return (output[0]['translation_text']) | |
| ##Translation | |
| demo = gr.Blocks() | |
| with demo: | |
| image_file = gr.Image(type="pil") | |
| examples = gr.Examples(examples=[["003.jpg"],["126.jpg"],["401.jpg"]],inputs=[image_file]) | |
| b1 = gr.Button("Recognize Image") | |
| text = gr.Textbox() | |
| b1.click(image_to_text, inputs=image_file, outputs=text) | |
| target = gr.Dropdown(LANGS,interactive=True,label="Target Language") | |
| b2 = gr.Button("Translation") | |
| out1 = gr.Textbox() | |
| b2.click(translation, inputs=[text,target], outputs=out1) | |
| #examples = gr.Examples(examples=[["003.jpg"]],inputs=[image_file]) | |
| demo.launch() |