Create app.py

app.py

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+from sentence_transformers import SentenceTransformer, util
+from PIL import Image
+import gradio as gr
+import requests
+
+def get_image_embedding(image):
+    image_model = SentenceTransformer('clip-ViT-B-32')
+    img_emb = image_model.encode(image)
+    return {"embedding": img_emb.tolist()}
+
+def get_text_embedding(text):
+    multilingual_text_model = SentenceTransformer('gte-Qwen2-1.5B-instruct')
+    text_emb = multilingual_text_model.encode(text)
+    print(text_emb)
+    print(type(text_emb))
+    print(text_emb.ndim)
+    return {"embedding": text_emb.tolist()}
+
+image_embedding = gr.Interface(fn=get_image_embedding, inputs=gr.Image(type="pil"), outputs=gr.JSON(api_name="image-embedding"), title="Image Embedding")
+text_embedding = gr.Interface(fn=get_text_embedding, inputs=gr.Textbox(), outputs=gr.JSON(api_name="text-embedding"), title="Text Embedding")
+
+space = gr.TabbedInterface([image_embedding, text_embedding], ["Image Embedding", "Text Embedding"])
+space.launch()