app.py

import json
import gradio as gr
from transformers import AutoModelWithLMHead, AutoTokenizer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("sumedh/t5-base-amazonreviews")
model = AutoModelForSeq2SeqLM.from_pretrained("sumedh/t5-base-amazonreviews")


def summarize_conversation(conversation, slider_value):

    summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")
    summarized_conversation = summarizer(conversation.strip(), min_length=int(slider_value))

    return summarized_conversation[0]["summary_text"]


def change_textbox(choice):    

    with open('examples.json') as json_file:
        examples = json.load(json_file)

    if choice == "Customer Dialog AmazonHelp":
        return gr.update(lines=8, visible=True, value=str(examples["dialog_1"]))
    elif choice == "Customer Dialog XBoxSupport":
        return gr.update(lines=8, visible=True, value=str(examples["dialog_2"]))
    elif choice == "Customer Dialog AppleSupport":
        return gr.update(lines=8, visible=True, value=str(examples["dialog_3"]))
    elif choice == "Fridge: Ice maker is not working":
        return gr.update(lines=8, visible=True, value=str(examples["text_ice_maker_not_working"]))
    elif choice == "Troubleshooting Iphone":
        return gr.update(lines=8, visible=True, value=str(examples["text_iphone_troubleshooting"]))
    else:
        return gr.update(lines=8, visible=True, value="")


with gr.Blocks() as demo:
    
    radio = gr.Radio(
        ["Free Text Input", "Customer Dialog AmazonHelp", "Customer Dialog XBoxSupport", "Customer Dialog AppleSupport", "Fridge: Ice maker is not working", "Troubleshooting Iphone"], label="What should the AI summarize?"
    )
    text = gr.Textbox(lines=2, interactive=True, placeholder="Your text here...")
    output = gr.Textbox(lines=2, interactive=True, placeholder="Your result here...")

    radio.change(fn=change_textbox, inputs=radio, outputs=text)
    slider_value = gr.Slider(minimum=20, maximum=142, value=42, randomize=False, label="Minimal length of summary.")
    button_clear = gr.Button("Clear")
    button_1 = gr.Button("Summarize")
    
    button_1.click(fn=summarize_conversation, inputs=[text, slider_value], outputs=output)

    button_clear.click(
        lambda: [c.update(value=None) for c in [text, output]],
        inputs=[],
        outputs=[text, output]
    )

demo.launch(share=True)