Initial commit

Former-commit-id: 5ca8e1d63727e7bcb8cab16542c763c47e48184a

src/web_demo.py

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+# coding=utf-8
+# Implements user interface in browser for LLaMA fine-tuned with PEFT.
+# Usage: python web_demo.py --checkpoint_dir path_to_checkpoint
+
+
+import torch
+import mdtex2html
+import gradio as gr
+
+from utils import ModelArguments, auto_configure_device_map, load_pretrained
+from transformers import HfArgumentParser
+
+
+parser = HfArgumentParser(ModelArguments)
+model_args, = parser.parse_args_into_dataclasses()
+model, tokenizer = load_pretrained(model_args)
+if torch.cuda.device_count() > 1:
+    from accelerate import dispatch_model
+    device_map = auto_configure_device_map(torch.cuda.device_count())
+    model = dispatch_model(model, device_map)
+else:
+    model = model.cuda()
+model.eval()
+
+
+"""Override Chatbot.postprocess"""
+
+def postprocess(self, y):
+    if y is None:
+        return []
+    for i, (message, response) in enumerate(y):
+        y[i] = (
+            None if message is None else mdtex2html.convert((message)),
+            None if response is None else mdtex2html.convert(response),
+        )
+    return y
+
+
+gr.Chatbot.postprocess = postprocess
+
+
+def parse_text(text): # copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT
+    lines = text.split("\n")
+    lines = [line for line in lines if line != ""]
+    count = 0
+    for i, line in enumerate(lines):
+        if "```" in line:
+            count += 1
+            items = line.split('`')
+            if count % 2 == 1:
+                lines[i] = f'<pre><code class="language-{items[-1]}">'
+            else:
+                lines[i] = f'<br></code></pre>'
+        else:
+            if i > 0:
+                if count % 2 == 1:
+                    line = line.replace("`", "\`")
+                    line = line.replace("<", "&lt;")
+                    line = line.replace(">", "&gt;")
+                    line = line.replace(" ", "&nbsp;")
+                    line = line.replace("*", "&ast;")
+                    line = line.replace("_", "&lowbar;")
+                    line = line.replace("-", "&#45;")
+                    line = line.replace(".", "&#46;")
+                    line = line.replace("!", "&#33;")
+                    line = line.replace("(", "&#40;")
+                    line = line.replace(")", "&#41;")
+                    line = line.replace("$", "&#36;")
+                lines[i] = "<br>"+line
+    text = "".join(lines)
+    return text
+
+
+def predict(input, chatbot, max_length, top_p, temperature, history):
+    chatbot.append((parse_text(input), ""))
+
+    inputs = tokenizer([input], return_tensors="pt")
+    inputs = inputs.to(model.device)
+    gen_kwargs = {
+        "do_sample": True,
+        "top_p": top_p,
+        "temperature": temperature,
+        "num_beams": 1,
+        "max_length": max_length,
+        "repetition_penalty": 1.0
+    }
+    with torch.no_grad():
+        generation_output = model.generate(**inputs, **gen_kwargs)
+    outputs = generation_output.tolist()[0][len(inputs["input_ids"][0]):]
+    response = tokenizer.decode(outputs, skip_special_tokens=True)
+    history = history + [(input, response)]
+    chatbot[-1] = (parse_text(input), parse_text(response))
+    yield chatbot, history
+
+
+def reset_user_input():
+    return gr.update(value='')
+
+
+def reset_state():
+    return [], []
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">ChatGLM-Efficient-Tuning</h1>""")
+
+    chatbot = gr.Chatbot()
+    with gr.Row():
+        with gr.Column(scale=4):
+            with gr.Column(scale=12):
+                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
+                    container=False)
+            with gr.Column(min_width=32, scale=1):
+                submitBtn = gr.Button("Submit", variant="primary")
+        with gr.Column(scale=1):
+            emptyBtn = gr.Button("Clear History")
+            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
+            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
+            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
+
+    history = gr.State([])
+
+    submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
+                    show_progress=True)
+    submitBtn.click(reset_user_input, [], [user_input])
+
+    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
+
+demo.queue().launch(server_name="0.0.0.0", share=False, inbrowser=True)