app.py

import gradio as gr
import os
from transformers import pipeline
title = "❤️🧠MindfulStory📖💾MemoryMaker"
examples = [
    ["Music and art make me feel"],
    ["Feel better each day when you awake by"],
    ["Feel better physically by"],
    ["Practicing mindfulness each day"],
    ["Be happier by"],
    ["Meditation can improve health"],
    ["Spending time outdoors"],
    ["Stress is relieved by quieting your mind, getting exercise and time with nature"],
    ["Break the cycle of stress and anxiety"],
    ["Feel calm in stressful situations"],
    ["Deal with work pressure"],
    ["Learn to reduce feelings of overwhelmed"]
]
from gradio import inputs
from gradio.inputs import Textbox
from gradio import outputs

# 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")

SCRIPT = """
<script>
if (!window.hasBeenRun) {
    window.hasBeenRun = true;
    console.log("should only happen once");
    document.querySelector("button.submit").click();
}
</script>
"""

# 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")

# 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("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)

SplitterInputBox = gr.inputs.Textbox(lines=5, label="Enter a sentence to get another sentence.")

def AIMemory(name: str, message: str):
    if name and message:
        with open(DATA_FILE, "a") as csvfile:
            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
            writer.writerow({"name": name, "message": message, "time": str(datetime.now())})
        commit_url = repo.push_to_hub()
    return {"name": name, "message": message, "time": str(datetime.now())}
    
parallelModel = gr.Parallel(generator1, generator2, generator3, inputs = SplitterInputBox, examples=examples,
            title="Mindfulness Story Generation with Persistent Dataset Memory",
            description=f"Mindfulness Story Generation with Persistent Dataset Memory",
            article=f"Memory Dataset URL: [{DATASET_REPO_URL}]({DATASET_REPO_URL})" )
parallelModel.launch(share=False)