Update Gradio-based demo

README.md

@@ -55,6 +55,11 @@ Read this in [English]()
   IOS：
 
 ## Demo & API
+### 基于Gradio的网页版Demo
+使用如下命令启动基于Gradio的网页版demo：
+```shell
+python demo/gradio_based_demo.py
+```
 
 ## 高效参数微调
 

demo/gradio_based_demo.py

@@ -0,0 +1,146 @@
+from typing import Dict
+from typing import List
+from typing import Tuple
+
+import gradio as gr
+import torch
+from threading import Thread
+from transformers import (
+    AutoModelForCausalLM, 
+    AutoTokenizer,
+    TextIteratorStreamer
+)
+
+
+# init model and tokenizer
+path = "openbmb/miniCPM-dpo-fp32"
+tokenizer = AutoTokenizer.from_pretrained(path)
+model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
+
+
+def hf_gen(query: str, top_p: float, temperature: float, max_dec_len: int):
+    """generate model output with huggingface api
+
+    Args:
+        query (str): actual model input.
+        top_p (float): only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
+        temperature (float): Strictly positive float value used to modulate the logits distribution.
+        max_dec_len (int): The maximum numbers of tokens to generate.
+
+    Yields:
+        str: real-time generation results of hf model
+    """    
+    inputs = tokenizer([query], return_tensors="pt").to("cuda")
+    streamer = TextIteratorStreamer(tokenizer)
+    generation_kwargs = dict(
+        inputs,
+        do_sample=True,
+        top_p=top_p,
+        temperature=temperature,
+        max_new_tokens=max_dec_len,
+        streamer=streamer,
+    )
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    answer = ""
+    for new_text in streamer:
+        answer += new_text
+        yield answer
+
+
+def generate(chat_history: List, query: str, top_p: float, temperature: float, max_dec_len: int):
+    """generate after hitting "submit" button
+
+    Args:
+        chat_history (List): [[q_1, a_1], [q_2, a_2], ..., [q_n, a_n]]. list that stores all QA records
+        query (str): query of current round
+        top_p (float): only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
+        temperature (float): strictly positive float value used to modulate the logits distribution.
+        max_dec_len (int): The maximum numbers of tokens to generate.
+
+    Yields:
+        List: [[q_1, a_1], [q_2, a_2], ..., [q_n, a_n], [q_n+1, a_n+1]]. chat_history + QA of current round.
+    """    
+    assert query != "", "Input must not be empty!!!"
+    # apply chat template
+    model_input = ""
+    for q, a in chat_history:
+        model_input += "<用户>" + q + "<AI>" + a
+    model_input += "<用户>" + query + "<AI>"
+    # yield model generation
+    chat_history.append([query, ""])
+    for answer in hf_gen(model_input, top_p, temperature, max_dec_len):
+        chat_history[-1][1] = answer[4 + len(model_input):].strip("</s>")
+        yield gr.update(value=""), chat_history
+
+
+def regenerate(chat_history: List, top_p: float, temperature: float, max_dec_len: int):
+    """re-generate the answer of last round's query
+
+    Args:
+        chat_history (List): [[q_1, a_1], [q_2, a_2], ..., [q_n, a_n]]. list that stores all QA records
+        top_p (float): only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
+        temperature (float): strictly positive float value used to modulate the logits distribution.
+        max_dec_len (int): The maximum numbers of tokens to generate.
+
+    Yields:
+        List: [[q_1, a_1], [q_2, a_2], ..., [q_n, a_n]]. chat_history
+    """    
+    assert len(chat_history) >= 1, "History is empty. Nothing to regenerate!!"
+    # apply chat template
+    model_input = ""
+    for q, a in chat_history[:-1]:
+        model_input += "<用户>" + q + "<AI>" + a
+    model_input += "<用户>" + chat_history[-1][0] + "<AI>"
+    # yield model generation
+    for answer in hf_gen(model_input, top_p, temperature, max_dec_len):
+        chat_history[-1][1] = answer[4 + len(model_input):].strip("</s>")
+        yield gr.update(value=""), chat_history
+
+
+def clear_history():
+    """clear all chat history
+
+    Returns:
+        List: empty chat history
+    """    
+    return []
+
+
+def reverse_last_round(chat_history):
+    """reverse last round QA and keep the chat history before
+
+    Args:
+        chat_history (List): [[q_1, a_1], [q_2, a_2], ..., [q_n, a_n]]. list that stores all QA records
+
+    Returns:
+        List: [[q_1, a_1], [q_2, a_2], ..., [q_n-1, a_n-1]]. chat_history without last round.
+    """    
+    assert len(chat_history) >= 1, "History is empty. Nothing to reverse!!"
+    return chat_history[:-1]
+
+# launch gradio demo
+with gr.Blocks(theme="soft") as demo:
+    gr.Markdown("""# MiniCPM Gradio Demo""")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            top_p = gr.Slider(0, 1, value=0.5, step=0.1, label="top_p")
+            temperature = gr.Slider(0.1, 2.0, value=1.0, step=0.1, label="temperature")
+            max_dec_len = gr.Slider(1, 1024, value=1024, step=1, label="max_dec_len")
+        with gr.Column(scale=5):
+            chatbot = gr.Chatbot(bubble_full_width=False, height=400)
+            user_input = gr.Textbox(label="User", placeholder="Input your query here!", lines=8)
+            with gr.Row():
+                submit = gr.Button("Submit")
+                clear = gr.Button("Clear")
+                regen = gr.Button("Regenerate")
+                reverse = gr.Button("Reverse")
+
+    submit.click(generate, inputs=[chatbot, user_input, top_p, temperature, max_dec_len], outputs=[user_input, chatbot])
+    regen.click(regenerate, inputs=[chatbot, top_p, temperature, max_dec_len], outputs=[user_input, chatbot])
+    clear.click(clear_history, inputs=[], outputs=[chatbot])
+    reverse.click(reverse_last_round, inputs=[chatbot], outputs=[chatbot])
+
+demo.queue()
+demo.launch(server_name="127.0.0.1", show_error=True)