Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+import spaces
+import torch
+import ast
+
+# Load the model
+model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B")
+
+@spaces.GPU
+def compute(queries_input, documents_input):
+    try:
+        # Convert string input to Python lists
+        queries = ast.literal_eval(queries_input)
+        documents = ast.literal_eval(documents_input)
+
+        # Validate input
+        if not isinstance(queries, list) or not isinstance(documents, list):
+            return "Inputs must be lists."
+
+        # Generate embeddings
+        query_embeddings = model.encode(queries, prompt_name="query")
+        document_embeddings = model.encode(documents)
+
+        # Compute similarity
+        similarity_matrix = torch.tensor(model.similarity(query_embeddings, document_embeddings))
+        return similarity_matrix.tolist()
+    except Exception as e:
+        return str(e)
+
+demo = gr.Interface(
+    fn=compute,
+    inputs=[
+        gr.Textbox(label="Queries (Python list, e.g. ['query1', 'query2'])"),
+        gr.Textbox(label="Documents (Python list, e.g. ['doc1', 'doc2'])")
+    ],
+    outputs="json",
+    title="embedding"
+)
+demo.launch()