File size: 3,700 Bytes
da29f14 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import gradio as gr
from resources import *
from transformers import pipeline
# Bellamy Bowie stuff
bellamy_bowie_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
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"]
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
# 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"]
with gr.Blocks() as aidademo:
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)
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")
bellamy_bowie_submit_button = gr.Button("Submit")
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)
with gr.Tab("Ellis Cappy"):
gr.HTML(ellis_cappy_description)
gr.Image("https://images.nightcafe.studio/jobs/1tLpG6zZANbrgG4ds8wF/1tLpG6zZANbrgG4ds8wF--4--andnn.jpg",
min_width=548)
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")
bellamy_bowie_submit_button.click(fn=bellamy_bowie_predict,
inputs=[bellamy_bowie_checkbox_input, bellamy_bowie_textbox_input],
outputs=bellamy_bowie_outputs)
aidademo.launch()
|