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app.py

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+#imported all required libraries
+import streamlit as st
+import torch
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
+
+
+#used a pretrained model hosted on huggingface
+loc = "ydshieh/vit-gpt2-coco-en"
+
+feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
+tokenizer = AutoTokenizer.from_pretrained(loc)
+model = VisionEncoderDecoderModel.from_pretrained(loc)
+model.eval()
+
+#defined a function for prediction
+
+def predict(image):
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+
+    with torch.no_grad():
+        output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
+
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+
+    return preds
+
+#defined a function for Streamlit App
+def app():
+    st.title("Image Captioner")
+    st.write("ViT and GPT2 are used to generate Image Caption for the uploaded image. COCO Dataset was used for training. This image captioning model might have some biases that I couldn’t figure during testing")
+    st.write("Upload an image or paste a URL to get predicted captions.")
+
+    upload_option = st.selectbox("Choose an option:", ("Upload Image", "Paste URL"))
+
+    if upload_option == "Upload Image":
+        uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg"])
+
+        if uploaded_file is not None:
+            image = Image.open(uploaded_file)
+            preds = predict(image)
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+            st.write("Predicted Caption:", preds)
+
+
+    elif upload_option == "Paste URL":
+        image_url = st.text_input("Enter Image URL")
+        if st.button("Submit") and image_url:
+            try:
+                response = requests.get(image_url, stream=True)
+                image = Image.open(BytesIO(response.content))
+                preds = predict(image)
+                st.image(image, caption="Image from URL", use_column_width=True)
+                st.write("Predicted Caption:", preds)
+            except:
+                st.write("Error: Invalid URL or unable to fetch image.")
+
+if __name__ == "__main__":
+    app()

requirement.txt

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+torch==2.0.1
+streamlit==1.22.0
+transformers==4.29.2
+requests
+pillow