Create app.py

app.py

@@ -0,0 +1,114 @@
+import subprocess
+import sys
+import os
+
+# Fonction pour installer un package si non présent
+def install_package(package_name):
+    subprocess.run([sys.executable, "-m", "pip", "install", package_name], check=True)
+
+# Vérifiez si torch est installé, sinon installez-le
+try:
+    import torch
+except ImportError:
+    print("Torch n'est pas installé. Installation de torch...")
+    install_package("torch")
+    import torch
+
+# Vérifiez si transformers est installé, sinon installez-le
+try:
+    from transformers import (
+        AutoModelForCausalLM,
+        AutoTokenizer,
+        TextIteratorStreamer,
+    )
+except ImportError:
+    print("Transformers n'est pas installé. Installation de transformers...")
+    install_package("transformers")
+    from transformers import (
+        AutoModelForCausalLM,
+        AutoTokenizer,
+        TextIteratorStreamer,
+    )
+
+# Installer flash-attn
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+
+import gradio as gr
+from threading import Thread
+
+# Obtenir le token d'authentification Hugging Face
+token = os.getenv("HF_TOKEN")
+if not token:
+    raise ValueError("Le token d'authentification HF_TOKEN n'est pas défini.")
+
+# Charger le modèle et le tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "CampAIgn/Phi-3-mini_16bit",
+    token=token,
+    trust_remote_code=True,
+)
+tok = AutoTokenizer.from_pretrained("HaitameLaf/Phi3-Game16bit", token=token)
+
+terminators = [tok.eos_token_id]
+
+# Vérifier la disponibilité du GPU
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+    print(f"Using GPU: {torch.cuda.get_device_name(device)}")
+else:
+    device = torch.device("cpu")
+    print("Using CPU")
+
+model = model.to(device)
+
+# Fonction de chat
+def chat(message, history, temperature, do_sample, max_tokens):
+    chat = [{"role": "user", "content": item[0]} for item in history]
+    chat.extend({"role": "assistant", "content": item[1]} for item in history if item[1])
+    chat.append({"role": "user", "content": message})
+    messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+    model_inputs = tok([messages], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+    
+    generate_kwargs = {
+        "input_ids": model_inputs.input_ids,
+        "streamer": streamer,
+        "max_new_tokens": max_tokens,
+        "do_sample": do_sample,
+        "temperature": temperature,
+        "eos_token_id": terminators,
+    }
+
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    partial_text = ""
+    for new_text in streamer:
+        partial_text += new_text
+        yield partial_text
+
+    yield partial_text
+
+# Configuration de Gradio
+demo = gr.ChatInterface(
+    fn=chat,
+    examples=[["Write me a poem about Machine Learning."]],
+    additional_inputs_accordion=gr.Accordion(
+        label="⚙️ Parameters", open=False, render=False
+    ),
+    additional_inputs=[
+        gr.Slider(minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature"),
+        gr.Checkbox(label="Sampling", value=True),
+        gr.Slider(minimum=128, maximum=4096, step=1, value=512, label="Max new tokens"),
+    ],
+    stop_btn="Stop Generation",
+    title="Chat With LLMs",
+    description="Now Running [CampAIgn/Phi-3-mini_16bit](https://huggingface.co/CampAIgn/Phi-3-mini_16bit)",
+)
+
+if __name__ == "__main__":
+    demo.launch()