import streamlit as st 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") # Directory path to the saved model on Google Drive 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") 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]) sentence = generated_caption text_to_remove = "<|endoftext|>" generated_caption = sentence.replace(text_to_remove, "") return generated_caption def generate_paragraph(caption): # Tokenize the caption inputs = tokenizer_2([caption], max_length=1024, truncation=True, padding="longest", return_tensors="pt") # Generate text output = model_2.generate(inputs.input_ids, attention_mask=inputs.attention_mask, max_length=200, num_beams=4, length_penalty=2.0, early_stopping=True) # 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') st.write('Upload an image to see what we have in store.') # create file uploader uploaded_file = st.file_uploader("Got You Covered, Upload your wish!, magic on the Way! ", type=["jpg", "jpeg", "png"]) # check if file has been uploaded if uploaded_file is not None: # load the image image = Image.open(uploaded_file).convert("RGB") # Image Captions string = generate_captions(uploaded_file) st.image(image, caption='The Uploaded File') st.write("First is first captions for your Photo : ", string) generated_paragraph = generate_paragraph(string) st.write(generated_paragraph) # run the app if __name__ == '__main__': app()