Create app.py

app.py

@@ -0,0 +1,109 @@
+import os
+import time
+import spaces
+import torch
+import gradio as gr
+from threading import Thread
+
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+TITLE = "<h1><center>Mistral-lab</center></h1>"
+
+PLACEHOLDER = """
+<center>
+<p>Chat with Mistral AI LLM.</p>
+</center>
+"""
+
+from huggingface_hub import snapshot_download
+from pathlib import Path
+
+mistral_models_path = Path.home().joinpath('mistral_models', '8B-Instruct')
+mistral_models_path.mkdir(parents=True, exist_ok=True)
+
+snapshot_download(repo_id="mistralai/Ministral-8B-Instruct-2410", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
+
+from mistral_inference.transformer import Transformer
+from mistral_inference.generate import generate
+
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import AssistantMessage, UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
+model = Transformer.from_folder(mistral_models_path)
+
+
+@spaces.GPU()
+def stream_chat(
+    message: str, 
+    history: list, 
+    temperature: float = 0.3, 
+    max_new_tokens: int = 1024, 
+):
+    print(f'message: {message}')
+    print(f'history: {history}')
+
+    conversation = []
+    for prompt, answer in history:
+        conversation.append(UserMessage(content=prompt))
+        conversation.append(AssistantMessage(content=answer))
+    conversation.append(UserMessage(content=message))
+
+    completion_request = ChatCompletionRequest(messages=conversation)
+    
+    tokens = tokenizer.encode_chat_completion(completion_request).tokens
+    
+    out_tokens, _ = generate(
+        [tokens], 
+        model, 
+        max_tokens=max_new_tokens, 
+        temperature=temperature,
+        eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+    
+    result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
+    
+    return result
+            
+chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
+
+with gr.Blocks(theme="ocean") as demo:
+    gr.HTML(TITLE)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
+    gr.ChatInterface(
+        fn=stream_chat,
+        chatbot=chatbot,
+        fill_height=True,
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+        additional_inputs=[
+            gr.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=0.3,
+                label="Temperature",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=128,
+                maximum=8192,
+                step=1,
+                value=1024,
+                label="Max new tokens",
+                render=False,
+            ),
+        ],
+        examples=[
+            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
+            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
+            ["Tell me a random fun fact about the Roman Empire."],
+            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
+        ],
+        cache_examples=False,
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()