import os
from pathlib import Path
import sqlite3

import gradio as gr
from datasets import Dataset, DatasetDict

# Connect to SQLite database
conn = sqlite3.connect('votes.db')
c = conn.cursor()

# Create table if it doesn't exist
c.execute('''CREATE TABLE IF NOT EXISTS votes
             (filename TEXT, uap_votes INTEGER, not_uap_votes INTEGER)''')

# Load image data from the folder
dataset_path = os.path.join(os.getcwd(), 'content/nuforc_reports')
dataset = DatasetDict.from_disk(dataset_path)
image_files = [example['file'] for example in dataset['train']]

# Function to vote for an image
def vote_for_image(filename, vote):
    if vote == 'UAP':
        c.execute("UPDATE votes SET uap_votes = uap_votes + 1 WHERE filename = ?", (filename,))
    elif vote == 'Not UAP':
        c.execute("UPDATE votes SET not_uap_votes = not_uap_votes + 1 WHERE filename = ?", (filename,))
    conn.commit()

# Get vote counts for an image
def get_vote_counts(filename):
    c.execute("SELECT uap_votes, not_uap_votes FROM votes WHERE filename = ?", (filename,))
    row = c.fetchone()
    if row:
        return row
    else:
        c.execute("INSERT INTO votes (filename, uap_votes, not_uap_votes) VALUES (?, 0, 0)", (filename,))
        conn.commit()
        return (0, 0)

# Gradio interface
image_select = gr.Dropdown(image_files, label="Select an image")
votes = gr.RenderedHTML()

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            image = gr.Image(value=f"{dataset_path}/{image_files[0]}", shape=(400, 400))
        with gr.Column():
            uap_vote = gr.Button("UAP")
            not_uap_vote = gr.Button("Not UAP")

    image_select.change(lambda x: f"{dataset_path}/{x}", inputs=image_select, outputs=image)
    uap_vote.click(vote_for_image, inputs=[image_select, gr.Const("UAP")], outputs=None)
    not_uap_vote.click(vote_for_image, inputs=[image_select, gr.Const("Not UAP")], outputs=None)

    vote_counts = gr.Column()
    image_select.change(lambda x: gr.Markdown(f"**UAP votes**: {get_vote_counts(x)[0]}\n**Not UAP votes**: {get_vote_counts(x)[1]}"), inputs=image_select, outputs=vote_counts)

demo.launch()import os
from pathlib import Path
import sqlite3

import gradio as gr
from datasets import Dataset, DatasetDict

# Connect to SQLite database
conn = sqlite3.connect('votes.db')
c = conn.cursor()

# Create table if it doesn't exist
c.execute('''CREATE TABLE IF NOT EXISTS votes
             (filename TEXT, uap_votes INTEGER, not_uap_votes INTEGER)''')

# Load image data from the folder
dataset_path = os.path.join(os.getcwd(), 'content/nuforc_reports')
dataset = DatasetDict.from_disk(dataset_path)
image_files = [example['file'] for example in dataset['train']]

# Function to vote for an image
def vote_for_image(filename, vote):
    if vote == 'UAP':
        c.execute("UPDATE votes SET uap_votes = uap_votes + 1 WHERE filename = ?", (filename,))
    elif vote == 'Not UAP':
        c.execute("UPDATE votes SET not_uap_votes = not_uap_votes + 1 WHERE filename = ?", (filename,))
    conn.commit()

# Get vote counts for an image
def get_vote_counts(filename):
    c.execute("SELECT uap_votes, not_uap_votes FROM votes WHERE filename = ?", (filename,))
    row = c.fetchone()
    if row:
        return row
    else:
        c.execute("INSERT INTO votes (filename, uap_votes, not_uap_votes) VALUES (?, 0, 0)", (filename,))
        conn.commit()
        return (0, 0)

# Gradio interface
image_select = gr.Dropdown(image_files, label="Select an image")
votes = gr.RenderedHTML()

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            image = gr.Image(value=f"{dataset_path}/{image_files[0]}", shape=(400, 400))
        with gr.Column():
            uap_vote = gr.Button("UAP")
            not_uap_vote = gr.Button("Not UAP")

    image_select.change(lambda x: f"{dataset_path}/{x}", inputs=image_select, outputs=image)
    uap_vote.click(vote_for_image, inputs=[image_select, gr.Const("UAP")], outputs=None)
    not_uap_vote.click(vote_for_image, inputs=[image_select, gr.Const("Not UAP")], outputs=None)

    vote_counts = gr.Column()
    image_select.change(lambda x: gr.Markdown(f"**UAP votes**: {get_vote_counts(x)[0]}\n**Not UAP votes**: {get_vote_counts(x)[1]}"), inputs=image_select, outputs=vote_counts)

demo.launch()