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
)

def generate_html() -> str:
    with open(DATA_FILE) as csvfile:
        reader = csv.DictReader(csvfile)
        rows = []
        for row in reader:
            rows.append(row)
        rows.reverse()
        if len(rows) == 0:
            return "no messages yet"
        else:
            html = "<div class='chatbot'>"
            for row in rows:
                html += "<div>"
                html += f"<span>{row['inputs']}</span>"
                html += f"<span class='outputs'>{row['outputs']}</span>"
                html += "</div>"
            html += "</div>"
            return html

    #store_message(message, response) # Save to dataset
    
#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)
#generator1 = gr.Interface.load("huggingface/gpt2-large", api_key=HF_TOKEN)

#greeter_1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 1"))
#greeter_2 = gr.Interface(lambda name: f"Greetings {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 2"))
#demo = gr.Parallel(greeter_1, greeter_2)

#generator1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=gr.Textbox(label="GPT2-Large")).load("huggingface/gpt2-large", api_key=HF_TOKEN)


#tbOutput = gr.Textbox(label="GPT Output")
#generator1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=[tbOutput]).load("huggingface/gpt2-large", api_key=HF_TOKEN)
#generator1 = 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)

#model_1_iface = gr.Interface(    fn=your_function_1,    inputs=gr.inputs.Textbox(),    outputs=gr.outputs.Label(num_top_classes=10))
#model_2_iface = gr.Interface(    fn= your_function_2,    inputs=gr.inputs.Textbox(),    outputs=gr.outputs.Label(num_top_classes=10),)

#generator1 = gr.Interface(fn=persist_memory,inputs=gr.inputs.Textbox(),outputs=gr.outputs.Label(num_top_classes=10) ).load("huggingface/gpt2-large",api_key=HF_TOKEN)
#OutputsGen=gr.outputs.Label(num_top_classes=10) 
#generator1 = gr.Interface(fn=persist_memory,inputs=[OutputsGen, OutputsGen],outputs=OutputsGen).load("huggingface/gpt2-large",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)

#MemoryChange=tbOutput.change(persist_memory,inputs=[tbOutput],outputs=gr.Textbox(label="PersistMemoryOutput"))
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())}
    
with gr.Blocks() as Memory:

#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})" )

    gr.Markdown("Mindfulness Story Generation with Persistent Dataset Memory")
    with gr.Row():
        inp = gr.Textbox(placeholder="What text would you like to extend with generation?")
        inp2 = gr.Textbox(placeholder="What text would you like to extend with generation?")
        out = gr.Textbox()
        out2 = gr.Textbox()
        out3 = gr.Textbox()
    btn = gr.Button("Run")
    btn.click(fn=AIMemory, inputs=[inp,inp], outputs=[out,out2,out3])

Memory.launch()

tbMemoryOutput = gr.Textbox(label="Memory Output")
btnSave = gr.Button("Save")
#btnSave.click(fn=persist_memory, inputs=[SplitterInputBox, tbOutput], outputs=tbMemoryOutput)
            
parallelModel.launch(share=False)