samidh commited on
Commit
aea822d
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1 Parent(s): f429c83

Update app.py

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Files changed (1) hide show
  1. app.py +18 -19
app.py CHANGED
@@ -2,7 +2,7 @@
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  import gradio as gr
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  import os
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- """
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  import torch
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  import torch.nn.functional as F
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  from peft import PeftConfig, PeftModel
@@ -27,7 +27,7 @@ model.merge_and_unload()
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  model = model.to(device)
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  tokenizer = AutoTokenizer.from_pretrained(base_model_name)
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- """
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  PROMPT = """
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  INSTRUCTIONS
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  ============
@@ -94,7 +94,6 @@ DEFAULT_CONTENT = "Put your content sample here."
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  # Function to make predictions
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  def predict(content, policy):
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- return "TEST"
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  input_text = PROMPT.format(policy=policy, content=content)
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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@@ -122,7 +121,7 @@ def predict(content, policy):
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  # Create the interface
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  with gr.Blocks() as demo:
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- gr.Markdown("# My Interactive Interface")
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  with gr.Row():
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  # Left column with inputs
@@ -135,21 +134,21 @@ with gr.Blocks() as demo:
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  output = gr.Textbox(label="Result")
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  submit_btn = gr.Button("Submit")
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  notes = gr.Markdown("""
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- # Usage Instructions
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-
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- This interface allows you to process two inputs and see the results.
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-
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- ## Features
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-
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- 1. Two input text boxes for versatile data entry
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- 2. Real-time processing
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- 3. Clear output display
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-
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- ## How to Use
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-
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- 1. Enter your first input in the left text box
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- 2. Enter your second input in the right text box
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- 3. Click the "Process" button to see results
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  """)
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  # Button below inputs
 
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  import gradio as gr
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  import os
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+
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  import torch
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  import torch.nn.functional as F
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  from peft import PeftConfig, PeftModel
 
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  model = model.to(device)
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  tokenizer = AutoTokenizer.from_pretrained(base_model_name)
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+
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  PROMPT = """
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  INSTRUCTIONS
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  ============
 
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  # Function to make predictions
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  def predict(content, policy):
 
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  input_text = PROMPT.format(policy=policy, content=content)
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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  # Create the interface
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  with gr.Blocks() as demo:
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+ gr.Markdown("# Zentropi CoPE Demo")
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  with gr.Row():
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  # Left column with inputs
 
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  output = gr.Textbox(label="Result")
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  submit_btn = gr.Button("Submit")
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  notes = gr.Markdown("""
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+ ## About CoPE
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+
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+ CoPE (the COntent Policy Evaluation engine) is a small language model capable of accurate content policy labeling. This is a **demo* of our initial release and should **NOT** be used for any production use cases.
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+
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+ ## How to Use
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+
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+ 1. Enter your content in the "Content" box.
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+ 2. Specify your policy in the "Policy" box.
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+ 3. Click "Submit" to see the results.
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+
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+ ## More Info
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+
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+ - [Give us feedback](https://forms.gle/BHpt6BpH2utaf4ez9) to help us improve
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+ - [Read our FAQ](https://docs.google.com/document/d/1Cp3GJ5k2I-xWZ4GK9WI7Xv8TpKdHmjJ3E9RbzP5Cc_Y/edit) to learn more about CoPE
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+ - [Join our mailing list](https://forms.gle/PCABrZdhTuXE9w9ZA) to keep in touch
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  """)
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  # Button below inputs