upload v1687590401 model

.ipynb_checkpoints/app-checkpoint.py

@@ -8,7 +8,7 @@ from huggingface_hub import Repository
 
 local_path = "hf_model"
 
-model_version = "v1687507574"
+model_version = "v1687590401"
 model_repo_id = "chansung/kerasnlp-gpt2-alpaca-pipeline"
 model_repo_url = f"https://huggingface.co/{model_repo_id}"
 

app.py

@@ -1,21 +1,14 @@
 from typing import Text, Any, Dict, Optional
 
-import json
-import copy
-
+import gradio as gr
 import tensorflow as tf
 import tensorflow_text
 from tensorflow.python.saved_model import tag_constants
 from huggingface_hub import Repository
 
-import gradio as gr
-from pingpong import PingPong
-from pingpong.gradio import GradioAlpacaChatPPManager
-from pingpong.context import CtxLastWindowStrategy
-
 local_path = "hf_model"
 
-model_version = "v1687507574"
+model_version = "v1687590401"
 model_repo_id = "chansung/kerasnlp-gpt2-alpaca-pipeline"
 model_repo_url = f"https://huggingface.co/{model_repo_id}"
 
@@ -30,382 +23,24 @@ _ = _clone_and_checkout(model_repo_url, local_path, model_version)
 model = tf.saved_model.load(local_path, tags=[tag_constants.SERVING])
 gpt_lm_predict_fn = model.signatures["serving_default"]
 
-STYLE = """
-.custom-btn {
-    border: none !important;
-    background: none !important;
-    box-shadow: none !important;
-    display: block !important;
-    text-align: left !important;
-}
-.custom-btn:hover {
-    background: rgb(243 244 246) !important;
-}
-
-.custom-btn-highlight {
-    border: none !important;
-    background: rgb(243 244 246) !important;
-    box-shadow: none !important;
-    display: block !important;
-    text-align: left !important;
-}
-
-#prompt-txt > label > span {
-    display: none !important;
-}
-#prompt-txt > label > textarea {
-    border: transparent;
-    box-shadow: none;
-}
-#chatbot {
-    height: 800px; 
-    overflow: auto;
-    box-shadow: none !important;
-    border: none !important;
-}
-#chatbot > .wrap {
-    max-height: 780px;
-}
-#chatbot + div {
-  border-radius: 35px !important;
-  width: 80% !important;
-  margin: auto !important;  
-}
-
-#left-pane {
-    background-color: #f9fafb;
-    border-radius: 15px;
-    padding: 10px;
-}
-
-#left-top {
-    padding-left: 10px;
-    padding-right: 10px;
-    text-align: center;
-    font-weight: bold;
-    font-size: large;    
-}
-
-#chat-history-accordion {
-    background: transparent;
-    border: 0.8px !important;  
-}
-
-#right-pane {
-  margin-left: 20px;
-  margin-right: 70px;
-}
-
-#initial-popup {
-    z-index: 100;
-    position: absolute;
-    width: 50%;
-    top: 50%;
-    height: 50%;
-    left: 50%;
-    transform: translate(-50%, -50%);
-    border-radius: 35px;
-    padding: 15px;
-}
-
-#initial-popup-title {
-    text-align: center;
-    font-size: 18px;
-    font-weight: bold;    
-}
-
-#initial-popup-left-pane {
-    min-width: 150px !important;
-}
-
-#initial-popup-right-pane {
-    text-align: right;
-}
-
-.example-btn {
-    padding-top: 20px !important;
-    padding-bottom: 20px !important;
-    padding-left: 5px !important;
-    padding-right: 5px !important;
-    background: linear-gradient(to bottom right, #f7faff, #ffffff) !important;
-    box-shadow: none !important;
-    border-radius: 20px !important;
-}
-
-.example-btn:hover {
-    box-shadow: 0.3px 0.3px 0.3px gray !important;
-}
-
-#example-title {
-  margin-bottom: 15px;
-}
-
-#aux-btns-popup {
-    z-index: 200;
-    position: absolute !important;
-    bottom: 75px !important;
-    right: 15px !important;
-}
-
-#aux-btns-popup > div {
-    flex-wrap: nowrap;
-    width: auto;
-    margin: auto;  
-}
-
-.aux-btn {
-    height: 30px !important;
-    flex-wrap: initial !important;
-    flex: none !important;
-    min-width: min(100px,100%) !important;
-    font-weight: unset !important;
-    font-size: 10pt !important;
-
-    background: linear-gradient(to bottom right, #f7faff, #ffffff) !important;
-    box-shadow: none !important;
-    border-radius: 20px !important;    
-}
-
-.aux-btn:hover {
-    box-shadow: 0.3px 0.3px 0.3px gray !important;
-}
-"""
-
-get_local_storage = """
-function() {
-  globalThis.setStorage = (key, value)=>{
-    localStorage.setItem(key, JSON.stringify(value));
-  }
-  globalThis.getStorage = (key, value)=>{
-    return JSON.parse(localStorage.getItem(key));
-  }
-
-  var local_data = getStorage('local_data');
-  var history = [];
-
-  if(local_data) {
-    local_data[0].pingpongs.forEach(element =>{ 
-      history.push([element.ping, element.pong]);
-    });
-  }
-  else {
-    local_data = [];
-    for (let step = 0; step < 10; step++) {
-      local_data.push({'ctx': '', 'pingpongs':[]});
-    }
-    setStorage('local_data', local_data);
-  }
-
-  if(history.length == 0) {
-    document.querySelector("#initial-popup").classList.remove('hide');
-  }
-  
-  return [history, local_data];
-}
-"""
-
-update_left_btns_state = """
-(v)=>{
-  document.querySelector('.custom-btn-highlight').classList.add('custom-btn');
-  document.querySelector('.custom-btn-highlight').classList.remove('custom-btn-highlight');
-
-  const elements = document.querySelectorAll(".custom-btn");
-
-  for(var i=0; i < elements.length; i++) {
-    const element = elements[i];
-    if(element.textContent == v) {
-      console.log(v);
-      element.classList.add('custom-btn-highlight');
-      element.classList.remove('custom-btn');
-      break;
-    }
-  }
-}""" 
-
-channels = [
-    "1st Channel",
-    "2nd Channel",
-    "3rd Channel",
-    "4th Channel",
-    "5th Channel",
-    "6th Channel",
-    "7th Channel",
-    "8th Channel",
-    "9th Channel",
-    "10th Channel"
-]
-channel_btns = []
-
-examples = [
-    "hello world", 
-    "what's up?", 
-    "this is GradioChat"
-]
-ex_btns = []
-
-def reset_chat(idx, ld):
-    res = [GradioAlpacaChatPPManager.from_json(json.dumps(ppm)) for ppm in ld]
-    res[idx].pingpongs = []
-        
-    return (
-        "",
-        [],
-        str(res),
-        gr.update(visible=True),
-        gr.update(interactive=False),
-    )
-
-def build_prompts(ppmanager):
-    dummy_ppm = copy.deepcopy(ppmanager)
-    
-    dummy_ppm.ctx = """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    return CtxLastWindowStrategy(3)(dummy_ppm)
-
-def add_pingpong(idx, ld, ping):
-    res = [GradioAlpacaChatPPManager.from_json(json.dumps(ppm)) for ppm in ld]
-    ppm = res[idx]
-    
-    ppm.add_pingpong(
-        PingPong(ping, "")
-    )
-    
-    prompt = tf.constant(build_prompts(ppm))
-    max_length = tf.constant(512, dtype="int64")
-    print(f"Prompt:\n{prompt}")
+def gen_text(prompt, max_length=256):
+    prompt = tf.constant(f"### Instruction:\n{prompt}\n\n### Response:\n")
+    max_length = tf.constant(max_length, dtype="int64")
     
     result = gpt_lm_predict_fn(
         prompt=prompt,
         max_length=max_length,
-    )['result'].numpy().decode('UTF-8')
-    result = result.split("### Response:")[-1].strip()
-
-    ppm.add_pong(result)
-    print(f"res:\n{str(res)}")
-    return "", ppm.build_uis(), str(res)
-
-def channel_num(btn_title):
-    choice = 0
-
-    for idx, channel in enumerate(channels):
-        if channel == btn_title:
-            choice = idx
-
-    return choice
-
-def set_chatbot(btn, ld):
-    choice = channel_num(btn)
-
-    res = [
-        GradioAlpacaChatPPManager.from_json(json.dumps(ppm_str))
-        for ppm_str in ld
-    ]
-    empty = len(res[choice].pingpongs) == 0
-    return (
-        res[choice].build_uis(), 
-        choice,
-        gr.update(visible=empty)
     )
 
-def set_example(btn):
-  return btn, gr.update(visible=False)
-
-def set_popup_visibility(ld, example_block):
-  return example_block
-
-with gr.Blocks(css=STYLE, elem_id='container-col') as demo:
-    idx = gr.State(0)
-    local_data = gr.JSON({},visible=False)
-
-    with gr.Row():
-        with gr.Column(scale=1, min_width=180):
-            gr.Markdown("GradioChat", elem_id="left-top")
-            
-            with gr.Column(elem_id="left-pane"):
-                with gr.Accordion("Histories", elem_id="chat-history-accordion"):
-                    channel_btns.append(gr.Button(channels[0], elem_classes=["custom-btn-highlight"]))
-    
-                    for channel in channels[1:]:
-                        channel_btns.append(gr.Button(channel, elem_classes=["custom-btn"]))
-            
-        with gr.Column(scale=8, elem_id="right-pane"):
-            with gr.Column(elem_id="initial-popup", visible=False) as example_block:
-                with gr.Row(scale=1):
-                    with gr.Column(elem_id="initial-popup-left-pane"):
-                        gr.Markdown("GradioChat", elem_id="initial-popup-title")
-                        gr.Markdown("Making the community's best AI chat models available to everyone.")
-                    with gr.Column(elem_id="initial-popup-right-pane"):
-                        gr.Markdown("Chat UI is now open sourced on Hugging Face Hub")
-                        gr.Markdown("check out the [↗ repository](https://huggingface.co/spaces/chansung/test-multi-conv)")
-
-                with gr.Column(scale=1):
-                    gr.Markdown("Examples")
-                    with gr.Row() as text_block:
-                        for example in examples:
-                            ex_btns.append(gr.Button(example, elem_classes=["example-btn"]))
-
-            with gr.Column(elem_id="aux-btns-popup", visible=True):
-                with gr.Row():
-                    # stop = gr.Button("Stop", elem_classes=["aux-btn"])
-                    # regenerate = gr.Button("Regenerate", elem_classes=["aux-btn"])
-                    clean = gr.Button("Clean", elem_classes=["aux-btn"])
+    return result['result'].numpy().decode('UTF-8').split("### Response:")[-1].strip()
 
-            chatbot = gr.Chatbot(elem_id='chatbot')
-            instruction_txtbox = gr.Textbox(
-                placeholder="Ask anything", label="",
-                elem_id="prompt-txt"
-            )
-
-    for btn in channel_btns:
-        btn.click(
-            set_chatbot,
-            [btn, local_data],
-            [chatbot, idx, example_block]        
-        ).then(
-            None, btn, None, 
-            _js=update_left_btns_state        
-        )
-    
-    for btn in ex_btns:
-        btn.click(
-            set_example,
-            [btn],
-            [instruction_txtbox, example_block]  
-        )
+with gr.Blocks() as demo:
+    instruction = gr.Textbox("Instruction")
+    output = gr.Textbox("Output", lines=5)
 
-    instruction_txtbox.submit(
-        lambda: gr.update(visible=False),
-        None,
-        example_block
-    ).then(
-        add_pingpong,
-        [idx, local_data, instruction_txtbox],
-        [instruction_txtbox, chatbot, local_data]
-    ).then(
-        None, local_data, None, 
-        _js="(v)=>{ setStorage('local_data',v) }"
+    instruction.submit(
+        lambda prompt: gen_text(prompt),
+        instruction, output
     )
 
-    clean.click(
-        reset_chat,
-        [idx, local_data],
-        [instruction_txtbox, chatbot, local_data, example_block]
-    ).then(
-        None, local_data, None, 
-        _js="(v)=>{ setStorage('local_data',v) }"
-    )
-    
-    demo.load(
-        None,
-        inputs=None,
-        outputs=[chatbot, local_data],
-        _js=get_local_storage,
-    )  
-
 demo.launch()

requirements.txt

@@ -1,4 +1,3 @@
 tensorflow
 tensorflow_text
-huggingface_hub
-bingbong
+huggingface_hub