clean code

app.py

@@ -3,21 +3,7 @@ import re
 import gradio as gr
 from pathlib import Path
 from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel
-# Pattern to ignore all the text after 2 or more full stops
-regex_pattern = "[.]{2,}"
-#sample = val_dataset[800]
-#model = model.cuda()
-#print(tokenizer.decode(model.generate(sample['pixel_values'].unsqueeze(0).cuda())[0]).replace('<|endoftext|>', '').split('\n')[0],'\n\n\n')
 
-
-def post_process(text):
-    try:
-        text = text.strip()
-        text = re.split(regex_pattern, text)[0]
-    except Exception as e:
-        print(e)
-        pass
-    return text
 def predict(image, max_length=64, num_beams=4):
     image = image.convert('RGB')
     pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
@@ -25,19 +11,6 @@ def predict(image, max_length=64, num_beams=4):
     with torch.no_grad():
         text = tokenizer.decode(model.generate(pixel_values.cpu())[0])
         text = text.replace('<|endoftext|>', '').split('\n')
-#[0],'\n\n\n'
-#text[0]
-        #text = model.generate(pixel_values.cpu())
-        #text = tokenizer.decode(text.replace('<|endoftext|>', '').split('\n')[0],'\n\n\n')
-       # output_ids = model.generate(
-        #    pixel_values,
-        #    max_length=max_length,
-         #   num_beams=num_beams,
-         #   return_dict_in_generate=True,
-        #).sequences
-        
-    #preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-    #pred = post_process(preds[0])
     return text[0]
     
 model_path = "team-indain-image-caption/hindi-image-captioning"
@@ -49,8 +22,6 @@ print("Loaded model")
 feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
 print("Loaded feature_extractor")
 tokenizer = AutoTokenizer.from_pretrained(model_path)
-#if model.decoder.name_or_path == "gpt2":
- #   tokenizer.pad_token = tokenizer.bos_token
 print("Loaded tokenizer")
 title = "Hindi Image Captioning"
 description = ""
@@ -65,6 +36,7 @@ interface = gr.Interface(
     description=description,
     #examples=example_images,
     live=True,
+    theme="darkpeach"
   
 )
 interface.launch()