Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+
+from transformers import GPT2TokenizerFast, ViTImageProcessor, VisionEncoderDecoderModel
+
+# load image captioning model and corresponding tokenizer and image processor
+model = VisionEncoderDecoderModel.from_pretrained("jojo-ai-mst/image-vision-cap")
+tokenizer = GPT2TokenizerFast.from_pretrained("jojo-ai-mst/image-vision-cap")
+image_processor = ViTImageProcessor.from_pretrained("jojo-ai-mst/image-vision-cap")
+
+
+def generate_caption(image):
+    image = Image.open(image)
+    pixel_values = image_processor(image, return_tensors="pt").pixel_values
+
+    # autoregressively generate caption (uses greedy decoding by default)
+    generated_ids = model.generate(pixel_values)
+    generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    print(generated_text)
+
+    return generated_text
+
+st.header("Welcome to Vision Caption Prototype",divider="rainbow")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+st.markdown("""
+    <style>
+    .stButton button {
+        background-color: green;
+        color: white;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+st.divider()
+
+if st.button("Get Answer"):
+    if uploaded_file is not None:
+        st.header("Result")
+        st.image(uploaded_file, caption=uploaded_file.name, use_column_width=True)
+
+        #answer = "The answer will be generated by AI"
+
+        caption = generate_caption(uploaded_file)
+
+        st.subheader(caption)
+    else:
+        st.write("Please upload an image and type a question.")