Create app.py test version (small scale example)

app.py

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+import os
+import glob
+import pandas as pd
+from PIL import Image
+from itertools import islice
+import earthview as ev
+import gradio as gr
+
+# this only gets the first file in the first shard - you can download more by editing this line
+filename = os.path.join("dataset", "satellogic", "train-00000-of-00065.parquet")
+# this returns an iterator for all files, not sorted
+# filenames = glob.glob(os.path.join("dataset", "satellogic", "*.parquet"))
+
+# loads a dataset with pandas, this loads a single file
+# for larger datasets you want to use `dask` which is significantly faster,
+# but I wanted to provide a simple version which only uses dependencies that have already been imported.
+data = pd.read_parquet(filename)
+
+# transforms a metadata sample to bounds and timestamps, handling revisits
+def item_to_bounds_timestamps(sample):
+    # unpack metadata
+    bounds_list = sample["metadata"]["bounds"]
+    timestamp_list = sample["metadata"]["timestamp"]
+    # each sample contains nested metadata
+    bounds = []
+    timestamps = []
+    # return two flat lists
+    for b, t in zip(bounds_list, timestamp_list):
+        bounds.append(b)
+        timestamps.append(t)
+    return bounds, timestamps
+
+# Create an empty list to store ratings
+ratings = []
+image_id = 0  # Initialize image ID counter
+bounds = []
+timestamps = []
+
+# Limit the number of images to process for the test
+num_images_to_process = 5
+
+# Iterate through the samples, display, rate, and store info
+data_iter = islice(data.iterrows(), num_images_to_process)
+for index, sample in data_iter:
+    rgb = sample["rgb"]
+    bounds_sample, timestamps_sample = item_to_bounds_timestamps(sample)
+    # iterate through the RGB images (revisits)
+    for i, img in enumerate(rgb):
+        print("Image ID:", image_id)
+        display(img)
+
+        # Get the rating from the user
+        while True:
+            try:
+                rating = int(input("Rate the image (0 or 1): "))
+                if rating in [0, 1]:
+                    break
+                else:
+                    print("Invalid rating. Please enter 0 or 1.")
+            except ValueError:
+                print("Invalid input. Please enter a number.")
+
+        # Store the rating and other info
+        ratings.append(rating)
+
+        # Store the bounds and timestamp
+        bounds.append(bounds_sample[i])
+        timestamps.append(timestamps_sample[i])
+
+        image_id += 1  # Increment image ID
+
+# Create a DataFrame from the collected data
+df = pd.DataFrame({
+    "image_id": range(image_id),
+    "rating": ratings,
+    "bounds": bounds,
+    "timestamp": timestamps
+})
+
+# Save the DataFrame to a CSV file
+df.to_csv("image_ratings_test.csv", index=False)
+
+print("Test complete. Ratings saved to image_ratings_test.csv")