Create app.py

app.py

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+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(uploaded_file)
+        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()