davanstrien HF staff commited on
Commit
dce9e22
Β·
1 Parent(s): 122f288

update inference logic

Browse files
Files changed (1) hide show
  1. app.py +58 -21
app.py CHANGED
@@ -6,30 +6,57 @@ import glob
6
  import os
7
  import uuid
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  from pathlib import Path
9
- import spaces
10
- import torch
11
- import transformers
12
  from huggingface_hub import CommitScheduler, hf_hub_download, login
13
- from transformers import AutoTokenizer, AutoModelForCausalLM
14
- from outlines import models, generate
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- from gradio import update
16
 
17
- model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
18
- tokenizer = AutoTokenizer.from_pretrained(model_id, add_special_tokens=True)
19
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
20
 
21
- @spaces.GPU(duration=120)
22
- def generate_blurb(history):
23
- model = models.transformers(model_id)
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- generator = generate.text(model)
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- resp = generator("Write a blurb for a book")
26
- return resp
27
 
28
  # Function to log blurb and vote
29
  def log_blurb_and_vote(blurb, vote):
30
  log_entry = {"timestamp": datetime.now().isoformat(), "blurb": blurb, "vote": vote}
31
  with open("blurb_log.jsonl", "a") as f:
32
  f.write(json.dumps(log_entry) + "\n")
 
33
  return f"Logged: {vote}"
34
 
35
 
@@ -38,19 +65,28 @@ tufte_theme = TufteInspired()
38
 
39
  # Create Gradio interface
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  with gr.Blocks(theme=tufte_theme) as demo:
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- gr.Markdown("<h1 style='text-align: center;'>Would you read it?</h1>")
42
  gr.Markdown(
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- "Click the button to generate a blurb for a made-up book, then vote on its quality."
 
 
44
  )
 
 
45
  with gr.Row():
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- generate_btn = gr.Button("Write a Blurb", variant="primary")
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- blurb_output = gr.Textbox(label="Generated Blurb", lines=5, interactive=False)
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- with gr.Row():
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  upvote_btn = gr.Button("πŸ‘ would read")
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  downvote_btn = gr.Button("πŸ‘Ž wouldn't read")
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- vote_output = gr.Textbox(label="Vote Status", interactive=False)
 
 
 
52
 
53
- generate_btn.click(generate_blurb, outputs=blurb_output)
 
 
54
  upvote_btn.click(
55
  lambda x: log_blurb_and_vote(x, "upvote"),
56
  inputs=blurb_output,
@@ -62,5 +98,6 @@ with gr.Blocks(theme=tufte_theme) as demo:
62
  outputs=vote_output,
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  )
64
 
 
65
  if __name__ == "__main__":
66
  demo.launch()
 
6
  import os
7
  import uuid
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  from pathlib import Path
9
+ from huggingface_hub import InferenceClient
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+ from openai import OpenAI
11
+ from huggingface_hub import get_token
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  from huggingface_hub import CommitScheduler, hf_hub_download, login
 
 
 
13
 
14
+ from prompts import detailed_genre_description_prompt, basic_prompt
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+ import random
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+ # TODOs
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+ # 1. Add a login button
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+ # 2. Prompt library expand
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+ # 3. log user if logged in
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+
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+
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+ client = OpenAI(
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+ base_url="https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct/v1",
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+ api_key=get_token(),
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+ )
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+
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+
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+ def generate_prompt():
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+ if random.choice([True, False]):
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+ return detailed_genre_description_prompt()
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+ else:
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+ return basic_prompt()
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+
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+
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+ def generate_blurb():
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+ max_tokens = random.randint(100, 1000)
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+ prompt = generate_prompt()
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+ print(prompt)
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+ chat_completion = client.chat.completions.create(
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+ model="tgi",
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+ messages=[
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+ {"role": "user", "content": prompt},
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+ ],
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+ stream=True,
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+ max_tokens=max_tokens,
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+ )
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+ full_text = ""
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+ for message in chat_completion:
50
+ full_text += message.choices[0].delta.content
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+ yield full_text
52
 
 
 
 
 
 
 
53
 
54
  # Function to log blurb and vote
55
  def log_blurb_and_vote(blurb, vote):
56
  log_entry = {"timestamp": datetime.now().isoformat(), "blurb": blurb, "vote": vote}
57
  with open("blurb_log.jsonl", "a") as f:
58
  f.write(json.dumps(log_entry) + "\n")
59
+ gr.Info("Thank you for voting!")
60
  return f"Logged: {vote}"
61
 
62
 
 
65
 
66
  # Create Gradio interface
67
  with gr.Blocks(theme=tufte_theme) as demo:
68
+ gr.Markdown("<h1 style='text-align: center;'>Would you read this book?</h1>")
69
  gr.Markdown(
70
+ """<p style='text-align: center;'>Looking for your next summer read?
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+ Would you read a book based on this LLM generated blurb? <br> Your vote will be added to <a href="https://example.com">this</a> Hugging Face dataset</p>"""
72
+ + """"""
73
  )
74
+ # gr.LoginButton(size="sm")
75
+ user_name = gr.Textbox(label="User Name", placeholder="Enter your name")
76
  with gr.Row():
77
+ generate_btn = gr.Button("Create a book", variant="primary")
78
+ blurb_output = gr.Markdown(label="Book blurb")
79
+ with gr.Row(visible=False) as voting_row:
80
  upvote_btn = gr.Button("πŸ‘ would read")
81
  downvote_btn = gr.Button("πŸ‘Ž wouldn't read")
82
+ vote_output = gr.Textbox(label="Vote Status", interactive=False, visible=False)
83
+
84
+ def show_voting_buttons(blurb):
85
+ return blurb, gr.Row(visible=True)
86
 
87
+ generate_btn.click(generate_blurb, outputs=blurb_output).then(
88
+ show_voting_buttons, inputs=blurb_output, outputs=[blurb_output, voting_row]
89
+ )
90
  upvote_btn.click(
91
  lambda x: log_blurb_and_vote(x, "upvote"),
92
  inputs=blurb_output,
 
98
  outputs=vote_output,
99
  )
100
 
101
+
102
  if __name__ == "__main__":
103
  demo.launch()