Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Embedding-0.6B")
+model = AutoModel.from_pretrained("Qwen/Qwen3-Embedding-0.6B")
+
+def get_embedding(text):
+    if len(text) > 250:
+        return "❌ Error: Input exceeds 250 character limit."
+
+    inputs = tokenizer(text, return_tensors="pt", truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        # Use [CLS] token embedding (or mean pooling)
+        embedding = outputs.last_hidden_state[:, 0, :].squeeze().tolist()
+
+    # Show only first 10 dimensions for readability
+    return f"✅ Embedding (first 10 values): {embedding[:10]}..."
+
+demo = gr.Interface(
+    fn=get_embedding,
+    inputs=gr.Textbox(label="Enter a sentence (max 250 characters)", max_lines=3, placeholder="Type your sentence here...", lines=2),
+    outputs="text",
+    title="Qwen3 Embedding Demo",
+    description="Generates sentence embeddings using Qwen/Qwen3-Embedding-0.6B. Input must be 250 characters or fewer."
+)
+
+if __name__ == "__main__":
+    demo.launch()