Update app.py

app.py

@@ -43,67 +43,3 @@ interface = gr.Interface(
     article="<h1>Created By:</h1>Mr. Karma Wangchuk<br>Lecturer<br>Information Technology Department<br>College of Science and Technology<br>Rinchending Phuentsholing<br>Chhukha Bhutan<br>",    
 )
 interface.launch(debug=True)
-# import torch 
-# import re 
-# import gradio as gr
-# from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel, AutoModelForSeq2SeqLM, pipeline
-# from transformers import ViTImageProcessor
-
-# device='cpu'
-# encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-# decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-# model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-
-# # feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
-
-# feature_extractor = ViTImageProcessor.from_pretrained(encoder_checkpoint)
-# tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
-# model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
-
-# mod = AutoModelForSeq2SeqLM.from_pretrained("KarmaCST/nllb-200-distilled-600M-en-to-dz")
-# tok = AutoTokenizer.from_pretrained("KarmaCST/nllb-200-distilled-600M-en-to-dz")
-
-
-# src_lang = 'eng_Latn'
-# tgt_lang = "dzo_Tibt"
-
-
-# def predict(image,max_length=64, num_beams=4):
-#     image = image.convert('RGB')
-#     image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
-#     clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
-#     # caption_ids = model.generate(image, max_length = max_length)[0]
-#     caption_ids = model.generate(image, max_length = max_length, clean_up_tokenization_spaces=False)[0] 
-#     caption_text = clean_text(tokenizer.decode(caption_ids)) 
-
-#     translation_pipeline = pipeline("translation",
-#                                     model=mod,
-#                                     tokenizer=tok,
-#                                     src_lang=src_lang,
-#                                     tgt_lang=tgt_lang)
-
-#     result = translation_pipeline(caption_text)
-#     return result[0]['translation_text']
-
-
-# # input = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
-# # output = gr.outputs.Textbox(type="auto",label="Captions")
-
-# # input = gr.Image(label="Upload any Image", type = 'pil', optional=True)
-# input = gr.Image(label="Upload any Image", type = 'pil')
-# output = gr.Textbox(type="auto",label="Captions")
-
-# examples = [f"example{i}.jpg" for i in range(1,7)]
-
-# title = "Image Captioning in Dzongkha "
-
-# interface = gr.Interface(
-#         fn=predict,
-#         inputs = input,
-#         theme="grass",
-#         outputs=output,
-#         examples = examples,
-#         title=title,
-#         article="<h1>Created By:</h1>Mr. Karma Wangchuk<br>Lecturer<br>Information Technology Department<br>College of Science and Technology<br>Rinchending Phuentsholing<br>Chhukha Bhutan<br>",
-#     )
-# interface.launch(debug=True)