Update app.py

app.py

@@ -1,3 +1,228 @@
+import os
+from threading import Thread
+from typing import Iterator
+
 import gradio as gr
+from langfuse import Langfuse
+from langfuse.decorators import observe
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import time
+
+MAX_MAX_NEW_TOKENS = 1048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "1096"))
+
+
+DESCRIPTION = """\
+# models/PartAI/Dorna-Llama3-8B-Instruct
+"""
+
+PLACEHOLDER = """
+<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://avatars.githubusercontent.com/u/39557177?v=4" style="width: 80%; max-width: 550px; height: auto; opacity: 0.80;  "> 
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Dorna-Llama3-8B-Instruct</h1>
+</div>
+"""
+
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Vazirmatn&display=swap');
+body, .gradio-container, .gr-button, .gr-input, .gr-slider, .gr-dropdown, .gr-markdown {
+    font-family: 'Vazirmatn', sans-serif !important;
+}
+._button {
+    font-size: 20px;
+}
+pre, code {
+    direction: ltr !important;
+    unicode-bidi: plaintext !important;
+}
+"""
+
+
+system_prompt = str(os.getenv("SYSTEM_PROMPT"))
+
+secret_key = str(os.getenv("LANGFUSE_SECRET_KEY"))
+public_key = str(os.getenv("LANGFUSE_PUBLIC_KEY"))
+host = str(os.getenv("LANGFUSE_HOST"))
+
+langfuse = Langfuse(
+  secret_key=secret_key,
+  public_key=public_key,
+  host=host
+)
+
+
+def execution_time_calculator(start_time, log=True):
+    delta = time.time() - start_time
+    if log:
+        print("--- %s seconds ---" % (delta))
+    return delta
+
+def token_per_second_calculator(tokens_count, time_delta):
+    return tokens_count/time_delta
+
+if not torch.cuda.is_available():
+    DESCRIPTION = "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+
+
+if torch.cuda.is_available():
+    model_id = "PartAI/Dorna-Llama3-8B-Instruct"
+    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    
+generation_speed = 0
+
+def get_generation_speed():
+    global generation_speed
+
+    return generation_speed
+
+@observe()
+def log_to_langfuse(message, chat_history, max_new_tokens, temperature, top_p, top_k, repetition_penalty, do_sample, generation_speed, model_outputs):
+    print(f"generation_speed: {generation_speed}")
+    return  "".join(model_outputs) 
+
+
+@spaces.GPU
+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,
+    do_sample: bool =True,
+) -> Iterator[str]:
+    global generation_speed
+    global system_prompt
+
+    conversation = []
+    if system_prompt:
+        conversation.append({"role": "system", "content": system_prompt})
+    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, 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=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {"input_ids": input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
+    )
+
+    start_time = time.time()
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    sum_tokens = 0
+    for text in streamer:
+        num_tokens = len(tokenizer.tokenize(text))
+        sum_tokens += num_tokens
+        
+        outputs.append(text)
+        yield "".join(outputs)
+
+    time_delta = execution_time_calculator(start_time, log=False)
+
+    generation_speed = token_per_second_calculator(sum_tokens, time_delta)
+
+    log_function = log_to_langfuse(
+        message=message,
+        chat_history=chat_history,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty,
+        do_sample=do_sample,
+        generation_speed=generation_speed,
+        model_outputs=outputs,
+    )
+
+
+
+
+
+chatbot = gr.Chatbot(placeholder=PLACEHOLDER, scale=1, show_copy_button=True, height="68%", rtl=True) #,  elem_classes=["chatbot"])
+chat_input = gr.Textbox(show_label=False, lines=2, rtl=True, placeholder="ورودی", show_copy_button=True, scale=4)
+submit_btn = gr.Button(variant="primary", value="ارسال", size="sm", scale=1, elem_classes=["_button"])
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    additional_inputs_accordion=gr.Accordion(label="ورودی‌های اضافی", open=False),
+    additional_inputs=[
+        gr.Slider(
+            label="حداکثر تعداد توکن ها",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
+            minimum=0.01,
+            maximum=4.0,
+            step=0.01,
+            value=0.5,
+        ),
+        gr.Slider(
+            label="Top-p",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.01,
+            value=0.9,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=20,
+        ),
+        gr.Slider(
+            label="جریمه تکرار",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.2,
+        ),
+        gr.Dropdown(
+            label="نمونه‌گیری",
+            choices=[False, True],
+            value=True)
+    ],
+    stop_btn="توقف",
+    chatbot=chatbot,
+    textbox=chat_input,
+    submit_btn=submit_btn,
+    retry_btn="🔄 تلاش مجدد",
+    undo_btn="↩️ بازگشت",
+    clear_btn="🗑️ پاک کردن",
+    title="درنا، محصول مرکز تحقیقات هوش مصنوعی پارت"
+)
+
+
+with gr.Blocks(css=custom_css, fill_height=False) as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
+
 
-gr.load("models/PartAI/Dorna-Llama3-8B-Instruct").launch()
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()