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import gradio as gr
from transformers import pipeline
from resources import *
bellamy_bowie_classifier_candidate_labels = ["manager", "engineer", "technician", "politician", "scientist", "student", "journalist", "marketeer", "spokesperson", "other"]
bellamy_bowie_classifier_candidate_labels_preselection = ["manager", "engineer", "technician", "politician", "scientist", "student", "journalist"]
bellamy_bowie_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
def bellamy_bowie_predict(candidate_labels_selected, sequence):
outputs = bellamy_bowie_classifier(sequence, candidate_labels_selected)
return dict(zip(outputs['labels'], outputs['scores'])) # Extract labels and scores from the outputs dictionary
def ellis_update(name, age):
return f"Welcome to Gradio, {name}! Are your really good {age} years old?"
# Ellis Cappy stuff
ellis_cappy_captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base", max_new_tokens=40)
def ellis_cappy_captionizer(img):
captions = ellis_cappy_captioner(img)
return captions[0]["generated_text"]
def marvin_update(origin, name):
# origin = type(origin)
return f"Welcome to Gradio, {name}! Are your really from {origin[0]}?"
with gr.Blocks() as demo:
gr.Markdown("Start typing below and then click **Run** to see the output.")
with gr.Tab("Bellamy Bowie"):
with gr.Row():
with gr.Column(scale=3):
gr.HTML(bellamy_bowie_description)
with gr.Column(scale=1):
gr.Image(bellamy_bowie_hero, label=None)
with gr.Row():
with gr.Column(scale=1):
bellamy_bowie_checkbox_input = gr.CheckboxGroup(choices=bellamy_bowie_classifier_candidate_labels, value=bellamy_bowie_classifier_candidate_labels_preselection, label="Target personas of your message", info="Recommendation: Don't change the preselection for your first analysis.")
bellamy_bowie_textbox_input = gr.Textbox(lines=10, placeholder="Your text goes here", label="Write or paste your message to classify")
with gr.Row():
bellamy_bowie_clear_button = gr.ClearButton(components=bellamy_bowie_textbox_input, value="Clear")
bellamy_bowie_submit_button = gr.Button("Submit", variant="primary")
with gr.Column(scale=1):
bellamy_bowie_outputs = gr.Label(label="Matching scores by personas")
gr.HTML(bellamy_bowie_note_quality)
with gr.Row():
with gr.Column(scale=1):
gr.Examples(bellamy_bowie_examples, inputs=[bellamy_bowie_textbox_input])
gr.HTML(bellamy_bowie_article)
bellamy_bowie_submit_button.click(fn=bellamy_bowie_predict, inputs=[bellamy_bowie_checkbox_input, bellamy_bowie_textbox_input], outputs=bellamy_bowie_outputs)
with gr.Tab("Urly & Murly Simmy"):
with gr.Row():
with gr.Column(scale=3):
gr.HTML(ellis_cappy_description)
with gr.Column(scale=1):
gr.Image(ellis_cappy_hero)
with gr.Row():
inp_01 = gr.Textbox(placeholder="What is your name?")
inp_02 = gr.Textbox(placeholder="What is your age?")
out_0 = gr.Textbox()
btn_0 = gr.Button("Run")
btn_0.click(fn=ellis_update, inputs=[inp_01, inp_02], outputs=out_0)
with gr.Tab("Ellis Cappy"):
with gr.Row():
with gr.Column(scale=3):
gr.HTML(ellis_cappy_description)
with gr.Column(scale=1):
gr.Image(ellis_cappy_hero)
with gr.Row():
with gr.Column(scale=1):
ellis_cappy_image_input = gr.Image(type="pil", label=None)
ellis_cappy_submit_button = gr.Button("Submit")
with gr.Column(scale=1):
ellis_cappy_textbox_output = gr.Textbox(label="Suggested caption", lines=2)
gr.HTML(ellis_cappy_note_quality)
with gr.Row():
with gr.Column(scale=1):
gr.Examples(ellis_cappy_examples, inputs=[ellis_cappy_image_input])
gr.HTML(ellis_cappy_article)
ellis_cappy_submit_button.click(fn=ellis_cappy_captionizer, inputs=ellis_cappy_image_input,
outputs=ellis_cappy_textbox_output, api_name="captionizer")
demo.launch()
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