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app.py

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+import json
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "thundax/Qwen2-1.5B-Sign"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map=device)
+
+with open("text2sign.json", 'r', encoding='utf-8') as f:
+    text2sign_dict = json.load(f)
+
+
+def do_predict(text):
+    input_text = f'Translate sentence into labels\n{text}\n'
+
+    model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
+
+    generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512)
+
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    signs = response_text.split(' ')
+    actions = {x: text2sign_dict[x] or '' for x in signs}
+
+    return json.dumps({'text': response_text, 'actions': actions}, ensure_ascii=False, indent=4)
+
+
+def run():
+    with gr.Blocks(title="Qwen2-1.5B-Sign") as app:
+        gr.HTML("<h1><center>Qwen2-1.5B-Sign</center></h1>")
+        input_text = gr.TextArea(label="Input", lines=2, value="你好，世界！")
+        submit_btn = gr.Button('Submit')
+        output_text = gr.TextArea(label="Output", lines=20)
+
+        submit_btn.click(do_predict, inputs=[input_text], outputs=[output_text])
+
+        app.launch()
+
+
+if __name__ == "__main__":
+    run()

requirements.txt

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+torch
+transformers
+gradio
+