Create app.py

app.py

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+import gradio as gr
+import onnxruntime as ort
+import numpy as np
+from transformers import AutoTokenizer
+
+# Load tokenizer (Qwen tokenizer)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B")
+
+# Load ONNX model
+session = ort.InferenceSession("/models/model.onnx")
+
+def chat_fn(user_input, history):
+    if not user_input.strip():
+        return history
+
+    # Tokenize input
+    inputs = tokenizer(user_input, return_tensors="np", padding=True)
+    ort_inputs = {session.get_inputs()[0].name: inputs["input_ids"].astype(np.int64)}
+
+    # Run inference
+    output = session.run(None, ort_inputs)[0]
+
+    # Decode model output
+    text = tokenizer.decode(output[0], skip_special_tokens=True)
+    history.append(("🧑‍💻 You: " + user_input, "🤖 Sam (Qwen3): " + text))
+    return history
+
+demo = gr.ChatInterface(fn=chat_fn, title="💬 Qwen3-0.6B-ONNX Demo",
+                        description="Running ONNX model on a prebuilt Docker Space (SmilyAI Style!)")
+
+demo.launch(server_name="0.0.0.0", server_port=7860)