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J-Antoine ZAGATO
commited on
Commit
•
9d80551
1
Parent(s):
10d46ff
Added toxicity comparison & flagging + refactoring
Browse files
app.py
CHANGED
@@ -1,3 +1,4 @@
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import torch
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import numpy as np
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@@ -9,6 +10,7 @@ from datasets import load_dataset
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from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM
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from transformers import BloomTokenizerFast, BloomForCausalLM
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DATASET = "allenai/real-toxicity-prompts"
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CHECKPOINTS = {
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@@ -140,19 +142,21 @@ def show_dataset(dataset):
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def update_dropdown(prompts):
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return gr.update(choices=random_sample(prompts))
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def show_text(text):
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new_text = "lol " + text
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return gr.update(visible = True, value=new_text)
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def process_user_input(model, input):
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warning = 'Please enter a valid prompt.'
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if input == None:
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return (
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gr.update(visible = True, value=generated),
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gr.update(visible=True)
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)
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def pass_to_textbox(input):
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@@ -161,21 +165,52 @@ def pass_to_textbox(input):
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def run_detoxify(text):
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results = Detoxify('original').predict(text)
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json_ready_results = {cat:float(score) for (cat,score) in results.items()}
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with gr.Blocks() as demo:
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gr.Markdown("# Project Interface proposal")
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dataset = gr.Variable(value=DATASET)
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prompts_var = gr.Variable(value=None)
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with gr.Row(equal_height=True):
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gr.Markdown("### 1. Select a prompt")
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input_text = gr.Textbox(label="Write your prompt below.", interactive=True)
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gr.Markdown("— or —")
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inspo_button = gr.Button('Click here if you need some inspiration')
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@@ -184,11 +219,8 @@ with gr.Blocks() as demo:
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randomize_button = gr.Button('Show another subset', visible=False)
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inspo_button.click(fn=show_dataset, inputs=dataset, outputs=[prompts_drop, randomize_button, prompts_var])
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randomize_button.click(fn=update_dropdown, inputs=prompts_var, outputs=prompts_drop)
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with gr.Column():
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gr.Markdown("### 2. Evaluate output")
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generate_button = gr.Button('Pick a model below and submit your prompt')
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@@ -199,16 +231,64 @@ with gr.Blocks() as demo:
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model_radio.change(fn=lambda value: value, inputs=model_radio, outputs=model_choice)
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output_text = gr.Textbox(label="Generated prompt.", visible=False)
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toxi_button = gr.Button("Run a toxicity analysis of the model's output", visible=False)
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toxi_scores = gr.JSON(visible=False)
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generate_button.click(fn=process_user_input,
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inputs=[model_choice, input_text],
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outputs=[output_text,toxi_button])
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#demo.launch(debug=True)
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if __name__ == "__main__":
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import os
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import torch
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import numpy as np
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from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM
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from transformers import BloomTokenizerFast, BloomForCausalLM
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HF_AUTH_TOKEN = os.environ.get('hf_token' or True)
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DATASET = "allenai/real-toxicity-prompts"
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CHECKPOINTS = {
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def update_dropdown(prompts):
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return gr.update(choices=random_sample(prompts))
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def process_user_input(model, input):
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warning = 'Please enter a valid prompt.'
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if input == None:
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generated = warning
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else:
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generated = generate(model, input)
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return (
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gr.update(visible = True, value=generated),
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gr.update(visible=True),
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gr.update(visible=True),
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gr.update(visible=True),
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gr.update(visible=True),
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input,
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generated
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)
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def pass_to_textbox(input):
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def run_detoxify(text):
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results = Detoxify('original').predict(text)
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json_ready_results = {cat:float(score) for (cat,score) in results.items()}
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return json_ready_results
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def compute_toxi_output(output_text):
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scores = run_detoxify(output_text)
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return (
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gr.update(value=scores, visible=True),
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gr.update(visible=True)
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)
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def compute_change(input, output):
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change_percent = round(((float(output)-input)/input)*100, 2)
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return change_percent
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def compare_toxi_scores(input_text, output_scores):
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input_scores = run_detoxify(input_text)
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json_ready_results = {cat:float(score) for (cat,score) in input_scores.items()}
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compare_scores = {
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cat:compute_change(json_ready_results[cat], output_scores[cat])
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for cat in json_ready_results
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for cat in output_scores
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}
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return (
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gr.update(value=json_ready_results, visible=True),
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gr.update(value=compare_scores, visible=True)
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)
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with gr.Blocks() as demo:
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gr.Markdown("# Project Interface proposal")
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gr.Markdown("### Write description and user instructions here")
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dataset = gr.Variable(value=DATASET)
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prompts_var = gr.Variable(value=None)
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input_var = gr.Variable(label="Input Prompt", value=None)
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output_var = gr.Variable(label="Output",value=None)
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flagging_callback = gr.HuggingFaceDatasetSaver(hf_token = HF_AUTH_TOKEN,
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dataset_name = "fsdlredteam/flagged",
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organization = "fsdlredteam",
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private = True )
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with gr.Row(equal_height=True):
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with gr.Column(): # input & prompts dataset exploration
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gr.Markdown("### 1. Select a prompt")
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input_text = gr.Textbox(label="Write your prompt below.", interactive=True, lines=4)
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gr.Markdown("— or —")
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inspo_button = gr.Button('Click here if you need some inspiration')
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randomize_button = gr.Button('Show another subset', visible=False)
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with gr.Column(): # Model choice & output
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gr.Markdown("### 2. Evaluate output")
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generate_button = gr.Button('Pick a model below and submit your prompt')
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model_radio.change(fn=lambda value: value, inputs=model_radio, outputs=model_choice)
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output_text = gr.Textbox(label="Generated prompt.", visible=False)
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with gr.Row(equal_height=True): # Flagging
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flagging_callback.setup([input_text, output_text, model_radio], "flagged_data_points")
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toxi_flag_button = gr.Button("Report toxic output here", visible=False)
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unexpected_flag_button = gr.Button("Report incorrect output here", visible=False)
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other_flag_button = gr.Button("Report other inappropriate output here", visible=False)
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with gr.Row(equal_height=True): # Toxicity buttons
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toxi_button = gr.Button("Run a toxicity analysis of the model's output", visible=False)
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toxi_button_compare = gr.Button("Compare toxicity on input and output", visible=False)
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with gr.Row(equal_height=True): # Toxicity scores
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toxi_scores_input = gr.JSON(label = "Detoxify classification of your input", visible=False)
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toxi_scores_output = gr.JSON(label="Detoxify classification of the model's output", visible=False)
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toxi_scores_compare = gr.JSON(label = "Percentage change between Input and Output", visible=False)
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inspo_button.click(fn=show_dataset,
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inputs=dataset,
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outputs=[prompts_drop, randomize_button, prompts_var])
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randomize_button.click(fn=update_dropdown,
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inputs=prompts_var,
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outputs=prompts_drop)
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generate_button.click(fn=process_user_input,
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inputs=[model_choice, input_text],
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outputs=[output_text,
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toxi_button,
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toxi_flag_button,
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unexpected_flag_button,
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other_flag_button,
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input_var,
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output_var])
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toxi_button.click(fn=compute_toxi_output,
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inputs=output_text,
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outputs=[toxi_scores_output, toxi_button_compare])
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toxi_button_compare.click(fn=compare_toxi_scores,
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inputs=[input_text, toxi_scores_output],
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outputs=[toxi_scores_input, toxi_scores_compare])
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toxi_flag_button.click(lambda *args: flagging_callback.flag(args, flag_option = "toxic"),
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inputs=[input_text, output_text, model_radio],
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outputs=None,
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preprocess=False)
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unexpected_flag_button.click(lambda *args: flagging_callback.flag(args, flag_option = "unexpected"),
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inputs=[input_text, output_text, model_radio],
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outputs=None,
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preprocess=False)
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other_flag_button.click(lambda *args: flagging_callback.flag(args, flag_option = "other"),
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inputs=[input_text, output_text, model_radio],
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outputs=None,
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preprocess=False)
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#demo.launch(debug=True)
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if __name__ == "__main__":
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