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requirements.txt

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+protobuf>=3.19.5,<3.20.1
+transformers>=4.26.1
+icetk
+cpm_kernels

web_demo.py

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+from transformers import AutoModel, AutoTokenizer
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
+model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+model = model.eval()
+
+MAX_TURNS = 20
+MAX_BOXES = MAX_TURNS * 2
+
+
+def predict(input, history=None):
+    if history is None:
+        history = []
+    response, history = model.chat(tokenizer, input, history)
+    updates = []
+    for query, response in history:
+        updates.append(gr.update(visible=True, value="用户：" + query))
+        updates.append(gr.update(visible=True, value="ChatGLM-6B：" + response))
+    if len(updates) < MAX_BOXES:
+        updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates))
+    return [history] + updates
+
+
+with gr.Blocks() as demo:
+    state = gr.State([])
+    text_boxes = []
+    for i in range(MAX_BOXES):
+        if i % 2 == 0:
+            text_boxes.append(gr.Markdown(visible=False, label="提问："))
+        else:
+            text_boxes.append(gr.Markdown(visible=False, label="回复："))
+
+    with gr.Row():
+        with gr.Column(scale=4):
+            txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
+        with gr.Column(scale=1):
+            button = gr.Button("Generate")
+    button.click(predict, [txt, state], [state] + text_boxes)
+demo.queue().launch(share=True)