Create app.py

app.py

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+import streamlit as st
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, GPT2Tokenizer
+import torch
+from PIL import Image
+
+# Load the model and tokenizer
+model = VisionEncoderDecoderModel.from_pretrained("ashok2216/vit-gpt2-image-captioning_COCO_FineTuned")
+processor = ViTImageProcessor.from_pretrained("ashok2216/vit-gpt2-image-captioning_COCO_FineTuned")
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+# Streamlit app title
+st.title("Image Captioning with ViT-GPT2 Model")
+st.write("Upload an image, and the model will generate a descriptive caption.")
+
+# File uploader for image input
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Load and display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Preprocess the image for the model
+    inputs = processor(images=image, return_tensors="pt")
+    pixel_values = inputs.pixel_values
+    
+    # Generate the caption
+    with st.spinner("Generating caption..."):
+        output = model.generate(pixel_values)
+        caption = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    # Display the generated caption
+    st.success("Generated Caption:")
+    st.write(caption)