import gradio as gr
import earthview as ev
import utils
import random
import pandas as pd
import os
from itertools import islice

# Configuration
chunk_size = 100  # Size of the chunks to shuffle
label_file = os.path.join(os.path.dirname(__file__), "labels.csv")  # Save CSV in the same directory as the script

# Load the Satellogic dataset (streaming)
dataset = ev.load_dataset("satellogic", streaming=True)
data_iter = iter(dataset)
shuffled_chunk = []  # Initialize an empty list to hold the current chunk
chunk_iter = None  # Initialize the chunk iterator

# Initialize or load labels DataFrame
if os.path.exists(label_file):
    labels_df = pd.read_csv(label_file)
else:
    labels_df = pd.DataFrame(columns=["image_id", "bounds", "rating", "google_maps_link"])

def get_next_image():
    global data_iter, labels_df, shuffled_chunk, chunk_iter

    while True:
        # If we don't have a current chunk or it's exhausted, get a new one
        if not shuffled_chunk or chunk_iter is None:
            chunk = list(islice(data_iter, chunk_size))
            if not chunk:  # If the dataset is exhausted, reset the iterator
                print("Dataset exhausted, resetting iterator.")
                data_iter = iter(ev.load_dataset("satellogic", streaming=True))
                chunk = list(islice(data_iter, chunk_size))
                if not chunk:
                    print("Still no data after reset.")
                    return None, "Dataset exhausted", None, None

            random.shuffle(chunk)
            shuffled_chunk = chunk
            chunk_iter = iter(shuffled_chunk)

        try:
            sample = next(chunk_iter)
            sample = ev.item_to_images("satellogic", sample)
            image = sample["rgb"][0]
            metadata = sample["metadata"]
            bounds = metadata["bounds"]
            google_maps_link = utils.get_google_map_link(sample, "satellogic")
            image_id = str(bounds)

            if image_id not in labels_df["image_id"].values:
                return image, image_id, bounds, google_maps_link
        except StopIteration:
            # Current chunk is exhausted, reset chunk variables to get a new one in the next iteration
            shuffled_chunk = []
            chunk_iter = None

def rate_image(image_id, bounds, rating):
    global labels_df

    new_row = pd.DataFrame(
        {
            "image_id": [image_id],
            "bounds": [bounds],
            "rating": [rating],
            "google_maps_link": [""], # this isn't necessary to pass to the function since we aren't updating it here.
        }
    )
    labels_df = pd.concat([labels_df, new_row], ignore_index=True)
    labels_df.to_csv(label_file, index=False)

    next_image, next_image_id, next_bounds, next_google_maps_link = get_next_image()
    return next_image, next_image_id, next_bounds, next_google_maps_link

# Gradio interface 
iface = gr.Interface(
    fn=rate_image,
    inputs=[
        gr.Textbox(label="Image ID", visible=False),
        gr.Textbox(label="Bounds", visible=False),
        gr.Radio(["Cool", "Not Cool"], label="Rating"),
        #gr.Textbox(label="Google Maps Link"), # Remove google maps link as an input
    ],
    outputs=[
        gr.Image(label="Satellite Image"),
        gr.Textbox(label="Image ID", visible=False),
        gr.Textbox(label="Bounds", visible=False),
        gr.Textbox(label="Google Maps Link", visible=True), # Add google maps link as an output
    ],
    title="TerraNomaly - Satellite Image Labeling",
    description="Rate satellite images as 'Cool' or 'Not Cool'.",
    live=False,
)

iface.launch(
    share=True
)