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import numpy as np
from PIL import Image
import streamlit as st
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from transformers import AutoTokenizer, VisionEncoderDecoderModel, ViTFeatureExtractor
# Load the Model,feature extractor and tokenizer
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
tokeniser = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
def generate_captions(image):
generated_caption = tokeniser.decode(model.generate(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
# Load the pre-trained model and tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
# Define the Streamlit app
def generate_paragraph(prompt):
# Tokenize the prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate the paragraph
output = model.generate(input_ids, max_length=200, num_return_sequences=1, early_stopping=True)
# Decode the generated output into text
paragraph = tokenizer.decode(output[0], skip_special_tokens=True)
return paragraph
# Streamlit app
def main():
# Set Streamlit app title and description
st.title("Paragraph Generation From Context of an Image")
st.subheader("Upload the Image to generate a paragraph.")
# create file uploader
uploaded_file = st.file_uploader("Choose an image...", 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")
# context as prompt
prompt = generate_captions(image)
st.write("The Context is:", prompt)
# display the image
st.image(uploaded_file)
generated_paragraph = generate_paragraph(prompt)
st.write(generated_paragraph)
if __name__ == "__main__":
main()
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