Update app.py

app.py

@@ -1,63 +1,147 @@
+import os
+from threading import Thread
+from typing import Iterator
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
+import spaces
+import torch
+from transformers import BitsAndBytesConfig, AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model_path = "vinai/PhoGPT-4B-Chat"
+
+config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+config.init_device = device
+
+quantization = BitsAndBytesConfig(load_in_8bit=True)
+
+model = AutoModelForCausalLM.from_pretrained(model_path, 
+                                             config=config, 
+                                             quantization_config =quantization, 
+                                             torch_dtype=torch.bfloat16, 
+                                             trust_remote_code=True,
+                                             low_cpu_mem_usage=True)
+
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+
+@spaces.GPU(duration=120)
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    conversation = []
+    for user, assistant in chat_history:
+        conversation.extend(
+            [
+                {"role": "user", "content": user},
+                {"role": "assistant", "content": assistant},
+            ]
+        )
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs ={
+        "input_ids": input_ids,
+        "streamer":streamer,
+        "max_new_tokens":max_new_tokens,
+        "do_sample":True,
+        "top_p":top_p,
+        "top_k":top_k,
+        "temperature":temperature,
+        "num_beams":1,
+        "repetition_penalty":repetition_penalty,
+        "eos_token_id":tokenizer.eos_token_id,
+        "pad_token_id":tokenizer.pad_token_id
+    }
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    chatbot=gr.Chatbot(height=500, label = "VN GPT", show_label=True),
+    textbox=gr.Textbox(placeholder="Nhập hội thoại tại đây", container=False, scale=7),
     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(
+            label="Độ dài token",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Độ sáng tạo",
             minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
+        ),
+        gr.Slider(
+            label="Lựa chọn từ dựa trên xác suất tích lũy",
+            minimum=0.05,
             maximum=1.0,
-            value=0.95,
             step=0.05,
-            label="Top-p (nucleus sampling)",
+            value=0.9,
         ),
+        gr.Slider(
+            label="Lựa chọn k từ có xác suất cao nhất",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
+        ),
+        gr.Slider(
+            label="Phạt lặp lại",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.2,
+        ),
+    ],
+    theme="soft",
+    stop_btn=None,
+    examples = [
+    ["Lợi ích của sữa mẹ ?"],
+    ["Sữa non là gì ?"],
+    ["Trẻ sơ sinh cần ngủ bao nhiêu giờ mỗi ngày?"],
+    ["Bao lâu nên cho trẻ sơ sinh bú một lần?"],
+    ["Khi nào nên bắt đầu cho trẻ ăn dặm?"],
+    ["Làm thế nào để giúp trẻ ngủ ngon vào ban đêm?"]
     ],
+
+    cache_examples=False,
+    title = "VN-GPT",
+    clear_btn="🗑️ Xóa",
+    undo_btn="↩️ Hoàn tác",
+    submit_btn="🚀 Gửi",
+    retry_btn="🔄 Thử lại",
+    additional_inputs_accordion="Tùy chỉnh nâng cao",
 )
 
 
 if __name__ == "__main__":
-    demo.launch()
+    chat_interface.queue(max_size=20).launch()