app.py

from goose3 import Goose
from resources import *
from sentence_transformers import SentenceTransformer, util
from transformers import pipeline
import gradio as gr


# Bellamy's stuff
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


# Urly's and Murly's stuff
# configuration
urly_murly_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
urly_murly_scraper = Goose()


def urly_murly_predict(url_1, url_2):
    # user input and text acquisition
    sequence_one = urly_murly_scraper.extract(url=url_1)
    sequence_two = urly_murly_scraper.extract(url=url_2)
    urly_murly_scraper.close()

    # #Compute embedding and similarity for both texts
    sentences = [sequence_one.cleaned_text, sequence_two.cleaned_text]
    embedding_1, embedding_2 = urly_murly_model.encode(sentences[0], convert_to_tensor=True), urly_murly_model.encode(sentences[1], convert_to_tensor=True)
    output = float(util.pytorch_cos_sim(embedding_1, embedding_2))

    # format result
    output_text = "The two webpages have a similarity score of {}.".format("%.2f" % (output * 100))

    return output_text, sequence_one.title, sequence_one.cleaned_text, sequence_two.title, sequence_two.cleaned_text


# Ellis' 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:
    # 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=2):
                gr.HTML(bellamy_bowie_description)
            with gr.Column(scale=1):
                gr.Image(bellamy_bowie_hero, show_label=False)
        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():
                    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")
        with gr.Row():
            with gr.Column(scale=1):
                gr.Examples(bellamy_bowie_examples, inputs=[bellamy_bowie_textbox_input])
                gr.HTML(bellamy_bowie_note_quality)
                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=1):
                gr.HTML(urly_murly_description)
            with gr.Column(scale=1):
                gr.Image(urly_murly_hero, show_label=False)
        with gr.Row():
            with gr.Column(scale=1):
                urly_murly_textbox_input_1 = gr.Textbox(label="URL of first webpage")
                urly_murly_textbox_input_2 = gr.Textbox(label="URL of second webpage")
                with gr.Row():
                    urly_murly_clear_button = gr.ClearButton(components=[urly_murly_textbox_input_1, urly_murly_textbox_input_2], value="Clear")
                    urly_murly_submit_button = gr.Button("Compare", variant="primary")
                gr.HTML(urly_murly_interpretation)
            with gr.Column(scale=1):
                urly_murly_textbox_output_1 = gr.Textbox(label="Similarity evaluation in the value range of [-100:100]")
                urly_murly_textbox_output_2 = gr.Textbox(label="First webpage: extracted title")
                urly_murly_textbox_output_3 = gr.Textbox(label="First webpage: extracted text")
                urly_murly_textbox_output_4 = gr.Textbox(label="Second webpage: extracted title")
                urly_murly_textbox_output_5 = gr.Textbox(label="Second webpage: extracted text")
        with gr.Row():
            with gr.Column(scale=1):
                gr.Examples(urly_murly_examples, inputs=[urly_murly_textbox_input_1, urly_murly_textbox_input_2])
                gr.HTML(urly_murly_about_scraping)
                gr.HTML(urly_murly_article)

        urly_murly_submit_button.click(fn=urly_murly_predict,
                                       inputs=[urly_murly_textbox_input_1, urly_murly_textbox_input_2],
                                       outputs=[urly_murly_textbox_output_1, urly_murly_textbox_output_2,
                                                urly_murly_textbox_output_3, urly_murly_textbox_output_4,
                                                urly_murly_textbox_output_5])

    with gr.Tab("Ellis Cappy"):
        with gr.Row():
            with gr.Column(scale=2):
                gr.HTML(ellis_cappy_description)
            with gr.Column(scale=1):
                gr.Image(ellis_cappy_hero, show_label=False)
        with gr.Row():
            with gr.Column(scale=1):
                ellis_cappy_image_input = gr.Image(type="pil", label=None)
                with gr.Row():
                    gr.ClearButton(components=ellis_cappy_image_input, value="Clear")
                    ellis_cappy_submit_button = gr.Button("Captionize", variant="primary")
            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")

aidademo.launch()