Initial version

app.py

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+import gradio as gr
+from PIL import Image
+from transformers import BridgeTowerForImageAndTextRetrieval, BridgeTowerProcessor
+
+model_id = "BridgeTower/bridgetower-large-itm-mlm-gaudi"
+processor = BridgeTowerProcessor.from_pretrained(model_id)
+model = BridgeTowerForImageAndTextRetrieval.from_pretrained(model_id)
+
+# Process an image
+def process(image, texts):
+    scores = {}
+    texts = texts.split(",")
+    for text in texts:
+        encoding = processor(image, text, return_tensors="pt")
+        outputs = model(**encoding)
+        scores[text] = "{:.2f}".format(outputs.logits[0, 1].item())
+        # sort scores in descending order
+        scores = dict(sorted(scores.items(), key=lambda item: item[1], reverse=True))
+    return scores
+
+
+# Inputs
+image = gr.Image(label="Image")
+texts = gr.Text(label="List of comma-separated texts")
+
+# Output
+scores = gr.JSON(label="Scores")
+
+description = "This Space lets you score a list of texts given an input image.\
+    This can be used to find the most relevant text for a given image, or for semantic search on images."
+
+iface = gr.Interface(
+    theme="huggingface",
+    description=description,
+    fn=process,
+    inputs=[image, texts],
+    outputs=scores,
+    examples=[],
+    allow_flagging="never",
+)
+
+iface.launch()

requirements.txt

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+git+https://github.com/huggingface/transformers
+torch
+requests
+Pillow