Create app.py

app.py

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+import gradio as gr
+import os, re, gc, time, sys, subprocess, typing, shutil, json, datetime, tempfile, safetensors, torch, threading, spaces
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from accelerate import Accelerator
+from huggingface_hub import login
+HF_TOKEN=os.environ.get('HF_TOKEN')
+login(token=HF_TOKEN)
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+os.environ["ZERO_GPU_PATCH_TORCH_DEVICE"] = "True"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+accelerator=Accelerator()
+
+TOKENIZER = accelerator.prepare(AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct"))
+MODEL = accelerator.prepare(AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct", torch_dtype=torch.bfloat16,))
+
+@spaces.GPU()
+def plex(pmpt, input_text):
+    model_inputs=""
+    generated_text=""
+    prompt = f"{pmpt}. The code: {input_text}"    
+    messages = [{"role": "system", "content": "Acting as an expert AI Web Development and Programming model. As an expert AI Web Development and Programming model, correct/modify/create/generate/complete the user provided code based on user request, output the code without comments. Only output code without additional prefixed or suffixed messages/comments/wrapping or additional text."}, {"role": "user", "content": prompt}]
+    text = TOKENIZER.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)
+    model_inputs = TOKENIZER([text], return_tensors="pt", return_token_type_ids=False).to(device)
+    generated_text = ""
+    for new_text in TOKENIZER.decode(MODEL.generate(**model_inputs, max_new_tokens=8192, do_sample=True, temperature=0.7, top_k=30, top_p=0.8,)[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True):
+        generated_text += new_text
+        yield generated_text.strip()
+    yield generated_text.strip()
+
+with gr.Blocks() as iface:
+    ins=gr.Code(lines=30)
+    pmpt=gr.Textbox()
+    btn=gr.Button("Assist")
+    btn.click(plex,[pmpt,ins],[ins])
+iface.queue()
+iface.launch(ssr_mode=False,share=False)