Create app.py

app.py

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+import streamlit as st
+from transformers import AutoProcessor, VisionEncoderDecoderModel
+import requests
+from PIL import Image
+import torch
+
+# Load processor and model
+st.title("Image to Text Captioning App")
+st.write("This app converts an image into a text description using the ViT-GPT2 model.")
+
+@st.cache_resource
+def load_model():
+    processor = AutoProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+    model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+    return processor, model
+
+processor, model = load_model()
+
+# Upload image
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocessing image
+    pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+    # Generate caption
+    generated_ids = model.generate(pixel_values)
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    st.write("Generated Caption: ")
+    st.success(generated_text)