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import gradio as gr
import os
# PersistDataset -----
import os
import csv
import gradio as gr
from gradio import inputs, outputs
import huggingface_hub
from huggingface_hub import Repository, hf_hub_download, upload_file
from datetime import datetime
# created new dataset as awacke1/MindfulStory.csv
#DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/MindfulStory.csv"
#DATASET_REPO_ID = "awacke1/MindfulStory.csv"
#DATA_FILENAME = "MindfulStory.csv"
#DATA_FILE = os.path.join("data", DATA_FILENAME)
HF_TOKEN = os.environ.get("HF_TOKEN")
# Download dataset repo using hub download
#try:
# hf_hub_download(
# repo_id=DATASET_REPO_ID,
# filename=DATA_FILENAME,
# cache_dir=DATA_DIRNAME,
# force_filename=DATA_FILENAME
# )
#except:
# print("file not found")
#def AIMemory(title: str, story: str):
# if title and story:
# with open(DATA_FILE, "a") as csvfile:
# writer = csv.DictWriter(csvfile, fieldnames=["title", "story", "time"])
# writer.writerow({"title": title, "story": story, "time": str(datetime.now())})
# uncomment line below to begin saving your changes
#commit_url = repo.push_to_hub()
# return ""
# Set up cloned dataset from repo for operations
#repo = Repository(
# local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
#)
#generator1 = gr.Interface.load("bigscience/bloom", api_key=HF_TOKEN)
generator1 = gr.Interface.load("huggingface/gpt2-large", api_key=HF_TOKEN)
generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B", api_key=HF_TOKEN)
generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B", api_key=HF_TOKEN)
def calculator(intro, operator, outro):
if operator == "add":
output = generator2(intro) + generator3(outro)
title = intro + " " + outro
# saved = AIMemory(title, output)
return output
elif operator == "subtract":
output = generator2(outro) + generator3(intro)
title = outro + " " + intro
# saved = AIMemory(title, output)
output = output.replace(intro, "").replace(outro, "")
return output
elif operator == "multiply":
output = generator1(intro) + generator2(outro) + generator3(intro)
title = intro + " " + outro + " " + intro
# saved = AIMemory(title, output)
return output
elif operator == "divide":
output = generator1(outro) + generator2(intro) + generator3(outro)
title = outro + " " + intro + " " + outro
# saved = AIMemory(title, output)
output = output.replace(intro, "").replace(outro, "")
return output
#with open('Mindfulness.txt', 'r') as file:
# context = file.read()
#contextBox = gr.Textbox(lines=3, default=context, label="Story starter")
examples = [
["Asynchronous Telemedicine", "multiply", "Provide remote care services live addressing provider shortages"],
["Ambient and emotion AI", "multiply", "rtificial intelligence showing empathy and compassion, reducing biases making us feel cared for and assist lifestyle"],
["import gradio as gr", "multiply", "import streamlit as st"],
["Skin Patch", "multiply", "Allow technology to measure blood pressure, glucose, reducing huge bulky devices"],
["Affordable vein scanner", "multiply", "View veins through skin"],
["Synthetic medical records", "multiply", "Create synthetic medical records using GANS trained to create synthetic data"],
["Blood draw devices used in clinical trials", "multiply", "So you dont have to go to physical location, engagement during trials"],
["Smart TVs being used for remote care", "multiply", "Video chat and recordings for remote care consultations"],
["Why does a chicken coop have two doors? Because if had four doors it would be a chicken sedan!", "multiply", "Why did the chicken cross the park? To get to the other slide."],
["What type of shoes do ninjas wear? Sneakers", "add", "Can a ninja bring a ninja star into the airport? Shuriken."],
["To save the planet with good looks and comedy find your", "multiply", "Everybody laughed at me when I told them I was going to be a comedian. I thought well, thats not bad for a start."]
]
demo = gr.Interface(
calculator,
[
"text",
gr.Radio(["add", "subtract", "multiply", "divide"]),
"text"
],
"text",
examples=examples,
article="Saved story memory dataset: https://huggingface.co/datasets/awacke1/MindfulStory.csv with available models to use from text gen: https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads",
live=True,
)
demo.launch() |