update

app.py

@@ -1,7 +1,50 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM
 
-def greet(name):
-    return "Hello " + name + "!!"
+
+def greet(input):
+
+    model_name = "Qwen/Qwen3-8B"
+
+    # load the tokenizer and the model
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+
+    tokenizer.save_pretrained("./qwen3")
+    model.save_pretrained("./qwen3")
+
+    # prepare the model input
+    prompt = "Give me a short introduction to large language model."
+    prompt = input
+    messages = [{"role": "user", "content": prompt}]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+        enable_thinking=True,  # Switches between thinking and non-thinking modes. Default is True.
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+    # conduct text completion
+    generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
+    output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()
+
+    # parsing thinking content
+    try:
+        # rindex finding 151668 (</think>)
+        index = len(output_ids) - output_ids[::-1].index(151668)
+    except ValueError:
+        index = 0
+
+    thinking_content = tokenizer.decode(
+        output_ids[:index], skip_special_tokens=True
+    ).strip("\n")
+    content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
+
+    # print("thinking content:", thinking_content)
+    # print("content:", content)
+
+    return "thinking content:" + thinking_content + "\n" + "content:" + content
 
 demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+demo.launch()