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(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 ""

with open('Mindfulness.txt', 'r') as file:
        context = file.read()
   
# 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)

def calculator(text1, operation, text2):
    if operation == "add":
        return generator1(text1) + generator2(text2)
    elif operation == "subtract":
        return replace(generator1(text1), generator2(text2), "")
    elif operation == "multiply":
        return generator1(text1) + generator2(text2) + generator2(text3) 
    elif operation == "divide":
        return replace(generator1(text1), generator3(text2), "")

demo = gr.Interface(
    calculator,
    [
        "text",
        gr.Radio(["add", "subtract", "multiply", "divide"]),
        "text"
    ],
    "text",
    live=True,
)
demo.launch()