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import gradio as gr
import os
from llama_cpp import Llama
import datetime
from huggingface_hub import hf_hub_download
#MODEL SETTINGS also for DISPLAY
convHistory = ''
modelfile = hf_hub_download(
repo_id=os.environ.get("REPO_ID", "slasiyal/deepseek-coder-1.3b-instruct.gguf"),
filename=os.environ.get("MODEL_FILE", "deepseek-coder-1.3b-instruct.gguf"),
)
repetitionpenalty = 1.15
contextlength=4096
logfile = 'logs.txt'
print("loading model...")
stt = datetime.datetime.now()
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path=modelfile, # Download the model file first
n_ctx=contextlength, # The max sequence length to use - note that longer sequence lengths require much more resources
#n_threads=2, # The number of CPU threads to use, tailor to your system and the resulting performance
)
dt = datetime.datetime.now() - stt
print(f"Model loaded in {dt}")
def writehistory(text):
with open(logfile, 'a') as f:
f.write(text)
f.write('\n')
f.close()
"""
gr.themes.Base()
gr.themes.Default()
gr.themes.Glass()
gr.themes.Monochrome()
gr.themes.Soft()
"""
def combine(a, b, c, d,e,f):
global convHistory
import datetime
SYSTEM_PROMPT = f"""{a}
"""
temperature = c
max_new_tokens = d
repeat_penalty = f
top_p = e
prompt = f"<|user|>\n{b}<|endoftext|>"
start = datetime.datetime.now()
generation = ""
delta = ""
prompt_tokens = f"Prompt Tokens: {len(llm.tokenize(bytes(prompt,encoding='utf-8')))}"
generated_text = ""
answer_tokens = ''
total_tokens = ''
for character in llm(prompt,
max_tokens=512,
stop=["</s>"],
temperature = 0.9,
repeat_penalty = 1,
top_p = 0.9, # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=False,
stream=True):
generation += character["choices"][0]["text"]
answer_tokens = f"Out Tkns: {len(llm.tokenize(bytes(generation,encoding='utf-8')))}"
total_tokens = f"Total Tkns: {len(llm.tokenize(bytes(prompt,encoding='utf-8'))) + len(llm.tokenize(bytes(generation,encoding='utf-8')))}"
delta = datetime.datetime.now() - start
yield generation, delta, prompt_tokens, answer_tokens, total_tokens
timestamp = datetime.datetime.now()
logger = f"""time: {timestamp}\n Temp: {temperature} - MaxNewTokens: {max_new_tokens} - RepPenalty: 1.5 \nPROMPT: \n{prompt}\nStableZephyr3B: {generation}\nGenerated in {delta}\nPromptTokens: {prompt_tokens} Output Tokens: {answer_tokens} Total Tokens: {total_tokens}\n\n---\n\n"""
writehistory(logger)
convHistory = convHistory + prompt + "\n" + generation + "\n"
print(convHistory)
return generation, delta, prompt_tokens, answer_tokens, total_tokens
#return generation, delta
# MAIN GRADIO INTERFACE
with gr.Blocks(theme='Medguy/base2') as demo: #theme=gr.themes.Glass() #theme='remilia/Ghostly'
#TITLE SECTION
with gr.Row(variant='compact'):
with gr.Column(scale=12):
gr.HTML("<center>"
+ "<h3>Prompt Engineering Playground!</h3>"
+ "<h1>🐦 deepseek-coder-1.3b </h2></center>")
gr.Image(value='https://modishcard.com/app/assets/icons/ModishCard_Logo6-02.svg', height=95, show_label = False,
show_download_button = False, container = False)
# INTERACTIVE INFOGRAPHIC SECTION
with gr.Row():
with gr.Column(min_width=80):
gentime = gr.Textbox(value="", placeholder="Generation Time:", min_width=50, show_label=False)
with gr.Column(min_width=80):
prompttokens = gr.Textbox(value="", placeholder="Prompt Tkn:", min_width=50, show_label=False)
with gr.Column(min_width=80):
outputokens = gr.Textbox(value="", placeholder="Output Tkn:", min_width=50, show_label=False)
with gr.Column(min_width=80):
totaltokens = gr.Textbox(value="", placeholder="Total Tokens:", min_width=50, show_label=False)
# PLAYGROUND INTERFACE SECTION
with gr.Row():
with gr.Column(scale=1):
gr.Markdown(
f"""
### Tunning Parameters""")
temp = gr.Slider(label="Temperature",minimum=0.0, maximum=1.0, step=0.01, value=0.42)
top_p = gr.Slider(label="Top_P",minimum=0.0, maximum=1.0, step=0.01, value=0.8)
repPen = gr.Slider(label="Repetition Penalty",minimum=0.0, maximum=4.0, step=0.01, value=1.2)
max_len = gr.Slider(label="Maximum output lenght", minimum=10,maximum=(contextlength-500),step=2, value=900)
gr.Markdown(
"""
Fill the System Prompt and User Prompt
And then click the Button below
""")
btn = gr.Button(value="🐦 Generate", variant='primary')
gr.Markdown(
f"""
- **Prompt Template**: OpenChat 🐦
- **Repetition Penalty**: {repetitionpenalty}
- **Context Lenght**: {contextlength} tokens
- **LLM Engine**: CTransformers
- **Model**: 🐦 deepseek-coder-1.3b
- **Log File**: {logfile}
""")
with gr.Column(scale=4):
txt = gr.Textbox(label="System Prompt", value = "", placeholder = "This models does not have any System prompt...",lines=1, interactive = False)
txt_2 = gr.Textbox(label="User Prompt", lines=6)
txt_3 = gr.Textbox(value="", label="Output", lines = 13, show_copy_button=True)
btn.click(combine, inputs=[txt, txt_2,temp,max_len,top_p,repPen], outputs=[txt_3,gentime,prompttokens,outputokens,totaltokens])
if __name__ == "__main__":
demo.launch(inbrowser=True) |