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| # Basic example for doing model-in-the-loop dynamic adversarial data collection | |
| # using Gradio Blocks. | |
| import random | |
| from urllib.parse import parse_qs | |
| import gradio as gr | |
| import requests | |
| from transformers import pipeline | |
| pipe = pipeline("sentiment-analysis") | |
| demo = gr.Blocks() | |
| with demo: | |
| total_cnt = 2 # How many examples per HIT | |
| dummy = gr.Textbox(visible=False) # dummy for passing assignmentId | |
| # We keep track of state as a Variable | |
| state_dict = {"assignmentId": "", "cnt": 0, "fooled": 0, "data": [], "metadata": {}, "response": ""} | |
| state = gr.Variable(state_dict) | |
| gr.Markdown("# DADC in Gradio example") | |
| gr.Markdown("Try to fool the model and find an example where it predicts the wrong label!") | |
| state_display = gr.Markdown(f"State: 0/{total_cnt} (0 fooled)") | |
| # Generate model prediction | |
| # Default model: distilbert-base-uncased-finetuned-sst-2-english | |
| def _predict(txt, tgt, state): | |
| pred = pipe(txt)[0] | |
| other_label = 'negative' if pred['label'].lower() == "positive" else "positive" | |
| pred_confidences = {pred['label'].lower(): pred['score'], other_label: 1 - pred['score']} | |
| pred["label"] = pred["label"].title() | |
| ret = f"Target: **{tgt}**. Model prediction: **{pred['label']}**\n\n" | |
| if pred["label"] != tgt: | |
| state["fooled"] += 1 | |
| ret += " You fooled the model! Well done!" | |
| else: | |
| ret += " You did not fool the model! Too bad, try again!" | |
| state["data"].append(ret) | |
| state["cnt"] += 1 | |
| done = state["cnt"] == total_cnt | |
| toggle_final_submit = gr.update(visible=done) | |
| toggle_example_submit = gr.update(visible=not done) | |
| new_state_md = f"State: {state['cnt']}/{total_cnt} ({state['fooled']} fooled)" | |
| return pred_confidences, ret, state, toggle_example_submit, toggle_final_submit, new_state_md | |
| # Input fields | |
| text_input = gr.Textbox(placeholder="Enter model-fooling statement", show_label=False) | |
| labels = ["Positive", "Negative"] | |
| random.shuffle(labels) | |
| label_input = gr.Radio(choices=labels, label="Target (correct) label") | |
| label_output = gr.Label() | |
| text_output = gr.Markdown() | |
| with gr.Column() as example_submit: | |
| submit_ex_button = gr.Button("Submit") | |
| with gr.Column(visible=False) as final_submit: | |
| submit_hit_button = gr.Button("Submit HIT") | |
| # Submit state to MTurk backend for ExternalQuestion | |
| # Update the URL below to switch from Sandbox to real data collection | |
| def _submit(state, dummy): | |
| query = parse_qs(dummy[1:]) | |
| print('yo') | |
| assert "assignment_id" in query, "No assignment ID provided, unable to submit" | |
| print('yo2') | |
| state["assignmentId"] = query["assignment_id"] | |
| url = "https://workersandbox.mturk.com/mturk/externalSubmit" | |
| x = requests.post(url, data=state) | |
| print('yo3') | |
| print(x) | |
| #print(x.text) | |
| return str(x) + " With assignmentID " + state["assignmentId"][0], state, dummy | |
| # Button event handlers | |
| submit_ex_button.click( | |
| _predict, | |
| inputs=[text_input, label_input, state], | |
| outputs=[label_output, text_output, state, example_submit, final_submit, state_display], | |
| ) | |
| response_output = gr.Markdown() | |
| submit_hit_button.click( | |
| _submit, | |
| inputs=[state, dummy], | |
| outputs=[response_output, state, dummy], | |
| _js="function(state, dummy) { console.log(window.location.search); return [state, window.location.search]; }", | |
| ) | |
| #gr.Markdown(state["response"].text if "response" in state and state["response"] is not None else "") | |
| demo.launch() |