Upload app.py

app.py

@@ -10,145 +10,130 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStream
 
 MAX_MAX_NEW_TOKENS = 2048
 DEFAULT_MAX_NEW_TOKENS = 2048
-total_count = 0
+total_count=0
 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "128000"))
 
 DESCRIPTION = """\
 # DeepSeek-R1-Chat
 
-This space demonstrates model [DeepSeek-Coder](https://huggingface.co/deepseek-ai/deepseek-coder-r1) by DeepSeek, a code model with 6XXB parameters fine-tuned for chat instructions.
+This space demonstrates model [DeepSeek-R1](https://huggingface.co/deepseek-ai/deepseek-r1) by DeepSeek, a code model with 6XXB parameters fine-tuned for chat instructions.
 
 **You can also try our R1 model in [official homepage](https://r1.deepseek.com/chat).**
 """
 
 if not torch.cuda.is_available():
-DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
 
 if torch.cuda.is_available():
-model_id = "deepseek-ai/deepseek-r1"
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype = torch.bfloat16, device_map = "auto")
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-tokenizer.use_default_system_prompt = False
-
+    model_id = "deepseek-ai/deepseek-r1"
+    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    tokenizer.use_default_system_prompt = False
+    
 
 
 @spaces.GPU
 def generate(
-  message: str,
-  chat_history: list[tuple[str, str]],
-  system_prompt: str,
-  max_new_tokens: int = 2048,
-  temperature: float = 0,
-  top_p: float = 0,
-  top_k: int = 50,
-  repetition_penalty: float = 2,
+    message: str,
+    chat_history: list[tuple[str, str]],
+    system_prompt: str,
+    max_new_tokens: int = 2048,
+    temperature: float = 0,
+    top_p: float = 0,
+    top_k: int = 50,
+    repetition_penalty: float = 2,
 ) -> Iterator[str]:
-global total_count
-total_count += 1
-print(total_count)
-os.system("nvidia-smi")
-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 "
-f" {
-  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 = False,
-  top_p = top_p,
-  top_k = top_k,
-  num_beams = 1,
-  # temperature=temperature,
-  repetition_penalty = repetition_penalty,
-  eos_token_id = 32021
-)
-t = Thread(target = model.generate, kwargs = generate_kwargs)
-t.start()
-
-outputs = []
-for text in streamer:
-outputs.append(text)
-yield "".join(outputs).replace("<|EOT|>","")
+    global total_count
+    total_count += 1
+    print(total_count)
+    os.system("nvidia-smi")
+    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=False,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=1,
+        # temperature=temperature,
+        repetition_penalty=repetition_penalty,
+        eos_token_id=32021
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs).replace("<|EOT|>","")
 
 
 chat_interface = gr.ChatInterface(
-  fn = generate,
-  additional_inputs = [
-    gr.Textbox(label = "System prompt", lines = 6),
-    gr.Slider(
-      label = "Max new tokens",
-      minimum = 1,
-      maximum = MAX_MAX_NEW_TOKENS,
-      step = 1,
-      value = DEFAULT_MAX_NEW_TOKENS,
-    ),
-    gr.Slider(
-      label = "Temperature",
-      minimum = 0,
-      maximum = 4.0,
-      step = 0.01,
-      value = 0,
-    ),
-    gr.Slider(
-      label = "Top-p (nucleus sampling)",
-      minimum = 0,
-      maximum = 4.0,
-      step = 0.01,
-      value = 0,
-    ),
-    gr.Slider(
-      label = "Top-k",
-      minimum = 1,
-      maximum = 1000,
-      step = 0.01,
-      value = 50,
-    ),
-    gr.Slider(
-      label = "Repetition penalty",
-      minimum = 0,
-      maximum = 2.0,
-      step = 0.01,
-      value = 2,
-    ),
-  ],
-  stop_btn = gr.Button("Stop"),
-  examples = [
-    ["implement snake game using pygame"],
-    ["Can you explain briefly to me what is the Python programming language?"],
-    ["write a program to find the factorial of a number"],
-  ],
+    fn=generate,
+    additional_inputs=[
+        gr.Textbox(label="System prompt", lines=6),
+        gr.Slider(
+            label="Max new tokens",
+            minimum=0,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=0.01,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        # gr.Slider(
+        #     label="Temperature",
+        #     minimum=0,
+        #     maximum=4.0,
+        #     step=0.01,
+        #     value=0,
+        # ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0,
+            maximum=1.0,
+            step=0.01,
+            value=0,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=0.01,
+            value=50,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.01,
+            value=2,
+        ),
+    ],
+    stop_btn=gr.Button("Stop"),
+    examples=[
+        ["implement snake game using pygame"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["write a program to find the factorial of a number"],
+    ],
 )
 
-with gr.Blocks(css = "style.css") as demo:
-gr.Markdown(DESCRIPTION)
-chat_interface.render()
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
 
 if __name__ == "__main__":
-demo.queue(max_size = 20).launch()
+    demo.queue(max_size=20).launch()