Update app.py

app.py

@@ -1,19 +1,19 @@
-import streamlit as st
+import torch
 import numpy as np
 from PIL import Image
-from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel, GPT2Tokenizer, GPT2LMHeadModel
-import torch
-from transformers import BartTokenizer, BartForConditionalGeneration
-
-# Load pre-trained BART model and tokenizer
-tokenizer_2 = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
-model_2 = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
+import streamlit as st
+from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel, BartTokenizer, BartForConditionalGeneration
 
-# Directory path to the saved model on Google Drive
+# pre-trained model to arrive at context
 model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 
+# pre-trained to arrive at description
+tokenizer_2 = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
+model_2 = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
+
+# function to generate context
 def generate_captions(image):
     image = Image.open(image).convert("RGB")
     generated_caption = tokenizer.decode(model.generate(feature_extractor(image, return_tensors="pt").pixel_values.to("cpu"))[0])
@@ -22,6 +22,7 @@ def generate_captions(image):
     generated_caption = sentence.replace(text_to_remove, "")
     return generated_caption
 
+# function to generate description
 def generate_paragraph(caption):
     # Tokenize the caption
     inputs = tokenizer_2([caption], max_length=1024, truncation=True, padding="longest", return_tensors="pt")
@@ -31,10 +32,8 @@ def generate_paragraph(caption):
 
     # Decode the generated output
     generated_text = tokenizer_2.decode(output[0], skip_special_tokens=True)
-    
     return generated_text
 
-
 # create the Streamlit app
 def app():
     st.title('Image from your Side, Detailed description from my site')