Update app.py

Just messing with this until I get a working demo

app.py

@@ -1,83 +1,81 @@
 import os
-import glob
 import pandas as pd
 from PIL import Image
 from itertools import islice
-import earthview as ev
+import earthview as ev  # Your custom library
 import gradio as gr
 
-# this only gets the first file in the first shard - you can download more by editing this line
+# Path to the first shard of the Satellogic dataset
 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.
+# Check if the file exists
+if not os.path.isfile(filename):
+    raise FileNotFoundError(f"{filename} not found")
+
+# Loads a dataset with pandas, this loads a single file
 data = pd.read_parquet(filename)
 
-# transforms a metadata sample to bounds and timestamps, handling revisits
+# Global variables to manage state
+current_image = None
+current_metadata_index = 0
+ratings = []
+image_id = 0
+bounds = []
+timestamps = []
+
+# Function to transform a metadata sample to bounds and timestamps
 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 = []
+# Function to load and display the next image
+def load_next_image():
+    global current_image, image_id, current_metadata_index, bounds, timestamps
 
-# Limit the number of images to process for the test
-num_images_to_process = 5
+    if current_metadata_index >= len(data):
+        return None, "No more images", None
 
-# 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"]
+    sample = data.iloc[current_metadata_index]
     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.")
+    # Use earthview library to convert arrays to PIL images
+    sample = ev.item_to_images("satellogic", sample.to_dict())
+    current_image = sample["rgb"][0]  # Get the first image
+    image_id += 1
 
-        # Store the rating and other info
-        ratings.append(rating)
+    return current_image, f"Image ID: {image_id}", bounds_sample[0]
 
-        # Store the bounds and timestamp
-        bounds.append(bounds_sample[i])
-        timestamps.append(timestamps_sample[i])
+# Function to handle rating submission
+def submit_rating(rating, bounds_str):
+    global image_id, current_metadata_index, ratings, bounds, timestamps
 
-        image_id += 1  # Increment image ID
+    ratings.append(rating)
+    bounds.append(bounds_str)
+    timestamps.append("timestamp")  # Use a valid timestamp if available
+    
+    current_metadata_index += 1
 
-# Create a DataFrame from the collected data
-df = pd.DataFrame({
-    "image_id": range(image_id),
-    "rating": ratings,
-    "bounds": bounds,
-    "timestamp": timestamps
-})
+    return load_next_image()
 
-# Save the DataFrame to a CSV file
-df.to_csv("image_ratings_test.csv", index=False)
+# Gradio Interface Layout
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            image_display = gr.Image(label="Satellite Image", type="pil")
+            image_id_display = gr.Textbox(label="Image ID")
+            bounds_display = gr.Textbox(label="Bounds")
+            load_button = gr.Button("Load Next Image")
+            load_button.click(fn=load_next_image, outputs=[image_display, image_id_display, bounds_display])
+        with gr.Column():
+            rating_radio = gr.Radio(["0", "1"], label="Rating (0 = No, 1 = Yes)")
+            submit_button = gr.Button("Submit Rating")
+            submit_button.click(fn=lambda rating, bounds_str: submit_rating(rating, bounds_str), inputs=[rating_radio, bounds_display], outputs=[image_display, image_id_display, bounds_display])
 
-print("Test complete. Ratings saved to image_ratings_test.csv")
+# Launch the Gradio interface
+demo.launch()