minor

app.py

@@ -1,64 +1,211 @@
+import os
+import torch
+from threading import Thread
+from typing import List, Optional, Tuple, Dict
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+import spaces
+from pathlib import Path
+from huggingface_hub import CommitScheduler
+import uuid
+import json
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Constants
+SYSTEM_PROMPT = """You are SmallThinker-3B, a helpful AI assistant. You try to follow instructions as much as possible while being accurate and brief."""
+device = "cuda" if torch.cuda.is_available() else "cpu"
+TITLE = "<h1><center>SmallThinker-3B Chat</center></h1>"
+MODEL_PATH = "PowerInfer/SmallThinker-3B-Preview"
 
+# Custom CSS with dark theme
+CSS = """
+.duplicate-button {
+    margin: auto !important;
+    color: white !important;
+    background: black !important;
+    border-radius: 100vh !important;
+}
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+h3 {
+    text-align: center;
+}
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+.chat-container {
+    height: 500px !important;
+    overflow-y: auto !important;
+    flex-direction: column !important;
+}
 
-    messages.append({"role": "user", "content": message})
+.messages-container {
+    flex-grow: 1 !important;
+    overflow-y: auto !important;
+    padding-right: 10px !important;
+}
 
-    response = ""
+.contain {
+    height: 100% !important;
+}
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+button {
+    border-radius: 8px !important;
+}
+"""
 
-        response += token
-        yield response
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.bfloat16,
+).to(device)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
 
+logs_id = os.getenv("LOGS_ID")
+logs_token = os.getenv("HF_LOGS_TOKEN")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+logs_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+logs_folder = logs_file.parent
+
+scheduler = CommitScheduler(
+    repo_id=logs_id,
+    repo_type="dataset",
+    folder_path=logs_folder,
+    path_in_repo="data",
+    every=5,
+    token=logs_token,
+    private=True,
 )
 
+@spaces.GPU
+def stream_chat(
+    message: str,
+    history: list,
+    temperature: float = 0.3,
+    max_new_tokens: int = 1024,
+    top_p: float = 1.0,
+    top_k: int = 20,
+    repetition_penalty: float = 1.2,
+):
+    # Create new history list with current message
+    new_history = history + [[message, ""]]
+    
+    conversation = []
+    # Only include previous messages in the conversation
+    for prompt, answer in history:
+        conversation.extend([
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": prompt},
+            {"role": "assistant", "content": answer},
+        ])
+    
+    conversation.append({"role": "user", "content": message})
+    
+    input_text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(tokenizer, timeout=40.0, skip_prompt=True, skip_special_tokens=True)
+    
+    generate_kwargs = dict(
+        input_ids=inputs,
+        max_new_tokens=max_new_tokens,
+        do_sample=False if temperature == 0 else True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        repetition_penalty=repetition_penalty,
+        streamer=streamer,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+    
+    with torch.no_grad():
+        thread = Thread(target=model.generate, kwargs=generate_kwargs)
+        thread.start()
+    
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        buffer = buffer.replace("\nUser", "")
+        buffer = buffer.replace("\nSystem", "")
+        new_history[-1][1] = buffer
+        yield new_history
+
+    with scheduler.lock:
+        with logs_file.open("a") as f:
+            f.write(json.dumps({"input": input_text.replace(SYSTEM_PROMPT, ""), "output": buffer.replace(SYSTEM_PROMPT, ""), "model": "SmallThinker-3B"}))
+            f.write("\n")
 
-if __name__ == "__main__":
+def clear_input():
+    return ""
+
+def add_message(message: str, history: list):
+    if message.strip() != "":
+        history = history + [[message, ""]]
+    return history
+
+def clear_session() -> Tuple[str, List]:
+    return '', []
+
+def main():
+    with gr.Blocks(css=CSS, theme="soft") as demo:
+        gr.HTML(TITLE)
+        gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
+        
+        with gr.Row():
+            with gr.Accordion(label="Chat Interface", open=True):
+                chatbot = gr.Chatbot(
+                    label='SmallThinker-3B',
+                    height=500,
+                    container=True,
+                    elem_classes=["chat-container"]
+                )
+                
+                with gr.Accordion(label="⚙️ Parameters", open=False):
+                    temperature = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.3, label="Temperature")
+                    max_new_tokens = gr.Slider(minimum=128, maximum=32768, step=128, value=16384, label="Max new tokens")
+                    top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="Top-p")
+                    top_k = gr.Slider(minimum=1, maximum=100, step=1, value=20, label="Top-k")
+                    repetition_penalty = gr.Slider(minimum=1.0, maximum=2.0, step=0.1, value=1.1, label="Repetition penalty")
+                
+                textbox = gr.Textbox(lines=1, label='Input')
+                
+                with gr.Row():
+                    clear_history = gr.Button("🧹 Clear History")
+                    submit = gr.Button("🚀 Send")
+                
+                # Chain of events for submit button
+                submit_event = submit.click(
+                    fn=add_message,
+                    inputs=[textbox, chatbot],
+                    outputs=chatbot,
+                    queue=False
+                ).then(
+                    fn=clear_input,
+                    outputs=textbox,
+                    queue=False
+                ).then(
+                    fn=stream_chat,
+                    inputs=[textbox, chatbot, temperature, max_new_tokens, top_p, top_k, repetition_penalty],
+                    outputs=chatbot,
+                    show_progress=True
+                )
+
+                # Chain of events for enter key
+                enter_event = textbox.submit(
+                    fn=add_message,
+                    inputs=[textbox, chatbot],
+                    outputs=chatbot,
+                    queue=False
+                ).then(
+                    fn=clear_input,
+                    outputs=textbox,
+                    queue=False
+                ).then(
+                    fn=stream_chat,
+                    inputs=[textbox, chatbot, temperature, max_new_tokens, top_p, top_k, repetition_penalty],
+                    outputs=chatbot,
+                    show_progress=True
+                )
+                
+                clear_history.click(fn=clear_session,
+                                  outputs=[textbox, chatbot])
+    
     demo.launch()
+
+if __name__ == "__main__":
+    main()