Update app.py

app.py

@@ -1,64 +1,150 @@
+import os
+from threading import Thread
+from typing import Iterator
+
 import gradio as gr
-from huggingface_hub import InferenceClient
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 
+# Configuration des limites de tokens
+MAX_MAX_NEW_TOKENS = 6048
+DEFAULT_MAX_NEW_TOKENS = 3024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+# Description pour l'interface utilisateur
+DESCRIPTION = """\
+# DeepSeek-6.7B-Chat
+
+This Space demonstrates model [DeepSeek-Coder](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) by DeepSeek, a code model with 6.7B parameters fine-tuned for chat instructions.
 """
-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")
 
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may run slowly on CPU.</p>"
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Chargement du modèle et du tokenizer
+model_id = "deepseek-ai/deepseek-coder-6.7b-instruct"
+if torch.cuda.is_available():
+    model = AutoModelForCausalLM.from_pretrained(model_id,
+                                                 torch_dtype=torch.bfloat16,
+                                                 device_map="auto")
+else:
+    model = AutoModelForCausalLM.from_pretrained(model_id)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.use_default_system_prompt = False
 
-    messages.append({"role": "user", "content": message})
 
-    response = ""
+@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list,
+    system_prompt: str,
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1,
+) -> Iterator[str]:
+    # Préparation de la conversation
+    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})
 
-    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
+    # Encodage des entrées
+    input_ids = tokenizer.apply_chat_template(conversation,
+                                              return_tensors="pt",
+                                              add_generation_prompt=True)
+    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)
 
-        response += token
-        yield response
+    # Création du flux de sortie
+    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,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
 
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs).replace("<|EOT|>", "")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
+
+# Interface utilisateur avec Gradio
+chat_interface = gr.ChatInterface(
+    fn=generate,
     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.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(
-            minimum=0.1,
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
             maximum=1.0,
-            value=0.95,
             step=0.05,
-            label="Top-p (nucleus sampling)",
+            value=0.9,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
         ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1,
+        ),
+    ],
+    stop_btn=None,
+    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"],
     ],
 )
 
+# Création du bloc Gradio
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue().launch(share=True)