Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+import torch
+
+# Load the model (Qwen3-Embedding-0.6B)
+# trust_remote_code is required for some Qwen architectures
+model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B", trust_remote_code=True)
+
+def embed_text(text, is_query=True):
+    """
+    API 1: Text Embedding
+    Qwen3 benefits from a 'query' prompt for retrieval tasks.
+    """
+    prompt_name = "query" if is_query else None
+    embedding = model.encode(text, prompt_name=prompt_name, convert_to_tensor=True)
+    return embedding.tolist()
+
+def calculate_similarity(text_a, text_b, is_query=True):
+    """
+    API 2: Embedding Similarity
+    Returns a float between 0 and 1 (clamped) representing the similarity.
+    """
+    prompt_name = "query" if is_query else None
+    
+    # Encode both texts
+    emb_a = model.encode(text_a, prompt_name=prompt_name, convert_to_tensor=True)
+    emb_b = model.encode(text_b, prompt_name=prompt_name, convert_to_tensor=True)
+    
+    # Compute Cosine Similarity
+    similarity = util.cos_sim(emb_a, emb_b).item()
+    
+    # Clamp to [0, 1] for "percentage" logic
+    score = max(0, min(1, similarity))
+    percentage = f"{score * 100:.2f}%"
+    
+    return score, percentage
+
+# Building the Gradio UI
+with gr.Blocks(title="Qwen3 Embedding API") as demo:
+    gr.Markdown("# Qwen3-Embedding-0.6B API & UI")
+    gr.Markdown("This space provides high-quality text embeddings and similarity scores.")
+
+    with gr.Tab("Text Embedding"):
+        with gr.Row():
+            with gr.Column():
+                input_text = gr.Textbox(label="Input Text", placeholder="Enter text to embed...")
+                is_query_toggle = gr.Checkbox(label="Is this a search query?", value=True, info="Uses Qwen's specific query prompt for better retrieval.")
+                btn_embed = gr.Button("Generate Embedding", variant="primary")
+            with gr.Column():
+                output_vec = gr.JSON(label="Embedding Vector (Truncated in UI)")
+        
+        btn_embed.click(fn=embed_text, inputs=[input_text, is_query_toggle], outputs=output_vec, api_name="embed")
+
+    with gr.Tab("Similarity Score"):
+        with gr.Row():
+            with gr.Column():
+                text_a = gr.Textbox(label="Text A", placeholder="First sentence...")
+                text_b = gr.Textbox(label="Text B", placeholder="Second sentence...")
+                is_query_sim = gr.Checkbox(label="Use query prompts?", value=True)
+                btn_sim = gr.Button("Compare Texts", variant="primary")
+            with gr.Column():
+                sim_float = gr.Number(label="Similarity Score (0-1)")
+                sim_percent = gr.Label(label="Match Percentage")
+        
+        # API returns both the float and the label string
+        btn_sim.click(fn=calculate_similarity, inputs=[text_a, text_b, is_query_sim], outputs=[sim_float, sim_percent], api_name="similarity")
+
+    gr.Markdown("""
+    ### How to use the API
+    You can call these endpoints programmatically using the Gradio Python Client:
+    ```python
+    from gradion_client import Client
+    client = Client("your-username/your-space-name")
+    
+    # API 1: Embedding
+    result = client.predict("Hello world", True, api_name="/embed")
+    
+    # API 2: Similarity
+    score, percent = client.predict("Text A", "Text B", True, api_name="/similarity")
+    ```
+    """)
+
+if __name__ == "__main__":
+    demo.launch()