Update

.gitignore

@@ -1,3 +1,4 @@
 .venv/
-model-checkpoint/
-__pycache__/
+model-checkpoints/**
+__pycache__/
+*.ipynb

README.md

@@ -1,6 +1,6 @@
 ---
-title: Thesis Demo
-emoji: ⚡
+title: Image Gelocation Thesis Demo
+emoji: 🌍
 colorFrom: yellow
 colorTo: indigo
 sdk: gradio

app.py

@@ -5,6 +5,11 @@ import torch
 import itertools
 import os
 import plotly.graph_objects as go
+import hashlib
+from PIL import Image
+import json
+
+os.environ["PYTHONHASHSEED"] = "42"
 
 
 CUDA_AVAILABLE = torch.cuda.is_available()
@@ -13,9 +18,9 @@ device = "cuda" if CUDA_AVAILABLE else "cpu"
 print(f"count={torch.cuda.device_count()}")
 print(f"current={torch.cuda.get_device_name(torch.cuda.current_device())}")
 
-continent_model = CLIPModel.from_pretrained("model-checkpoints/continent")
-country_model = CLIPModel.from_pretrained("model-checkpoints/country")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14-336")
+continent_model = CLIPModel.from_pretrained("jrheiner/thesis-clip-geoloc-continent", token=os.getenv("token"))
+country_model = CLIPModel.from_pretrained("jrheiner/thesis-clip-geoloc-country", token=os.getenv("token"))
+processor = CLIPProcessor.from_pretrained("jrheiner/thesis-clip-geoloc-continent", token=os.getenv("token"))
 continent_model = continent_model.to(device)
 country_model = country_model.to(device)
 
@@ -24,51 +29,161 @@ continents = ["Africa", "Asia", "Europe",
               "North America", "Oceania", "South America"]
 countries_per_continent = {
     "Africa": [
-        "Algeria", "Angola", "Benin", "Botswana", "Burkina Faso", "Burundi", "Cabo Verde", "Cameroon",
-        "Central African Republic", "Congo", "Democratic Republic of the Congo",
-        "Djibouti", "Egypt", "Equatorial Guinea", "Eritrea", "Eswatini", "Ethiopia", "Gabon",
-        "Gambia", "Ghana", "Guinea", "Guinea-Bissau", "Ivory Coast", "Kenya", "Lesotho", "Liberia",
-        "Libya", "Madagascar", "Malawi", "Mali", "Mauritania", "Mauritius", "Morocco", "Mozambique",
-        "Namibia", "Niger", "Nigeria", "Rwanda", "Sao Tome and Principe", "Senegal", "Seychelles",
-        "Sierra Leone", "Somalia", "South Africa", "Sudan", "Tanzania", "Togo",
-        "Tunisia", "Uganda", "Zambia", "Zimbabwe"
+        "Botswana", "Eswatini", "Ghana", "Kenya", "Lesotho", "Nigeria", "Senegal",
+        "South Africa", "Rwanda", "Uganda", "Tanzania", "Madagascar", "Djibouti", 
+        "Mali", "Libya", "Morocco", "Somalia", "Tunisia", "Egypt", "Réunion"
     ],
     "Asia": [
-        "Afghanistan", "Armenia", "Azerbaijan", "Bahrain", "Bangladesh", "Bhutan", "Brunei",
-        "Cambodia", "China", "Cyprus", "Georgia", "India", "Indonesia", "Iran", "Iraq",
-        "Israel", "Japan", "Jordan", "Kazakhstan", "Kuwait", "Kyrgyzstan", "Laos", "Lebanon",
-        "Malaysia", "Maldives", "Mongolia", "Myanmar", "Nepal", "North Korea", "Oman", "Pakistan",
-        "Palestine", "Philippines", "Qatar", "Russia", "Saudi Arabia", "Singapore", "South Korea",
-        "Sri Lanka", "Syria", "Taiwan", "Tajikistan", "Thailand", "Timor-Leste", "Turkey",
-        "Turkmenistan", "United Arab Emirates", "Uzbekistan", "Vietnam", "Yemen"
+        "Bangladesh", "Bhutan", "Cambodia", "China", "India", "Indonesia", "Israel", 
+        "Japan", "Jordan", "Kyrgyzstan", "Laos", "Malaysia", "Mongolia", "Nepal", 
+        "Palestine", "Philippines", "Singapore", "South Korea", "Sri Lanka", 
+        "Taiwan", "Thailand", "United Arab Emirates", "Vietnam", "Afghanistan", 
+        "Azerbaijan", "Cyprus", "Iran", "Syria", "Tajikistan", "Turkey", "Russia", 
+        "Pakistan", "Hong Kong"
     ],
     "Europe": [
-        "Albania", "Armenia", "Austria", "Azerbaijan", "Belarus", "Belgium", "Bosnia and Herzegovina",
-        "Bulgaria", "Croatia", "Cyprus", "Czech Republic", "Denmark", "Estonia", "Finland", "France",
-        "Georgia", "Germany", "Greece", "Hungary", "Iceland", "Ireland", "Italy", "Kazakhstan",
-        "Kosovo", "Latvia", "Liechtenstein", "Lithuania", "Luxembourg", "Malta", "Moldova", "Monaco",
-        "Montenegro", "Netherlands", "North Macedonia", "Norway", "Poland", "Portugal", "Romania",
-        "Russia", "San Marino", "Serbia", "Slovakia", "Slovenia", "Spain", "Sweden", "Switzerland",
-        "Turkey", "Ukraine", "United Kingdom"
+        "Albania", "Andorra", "Austria", "Belgium", "Bulgaria", "Croatia", "Czechia", 
+        "Denmark", "Estonia", "Finland", "France", "Germany", "Greece", "Hungary", 
+        "Iceland", "Ireland", "Italy", "Latvia", "Lithuania", "Luxembourg", 
+        "Montenegro", "Netherlands", "North Macedonia", "Norway", "Poland", 
+        "Portugal", "Romania", "Russia", "Serbia", "Slovakia", "Slovenia", "Spain", 
+        "Sweden", "Switzerland", "Ukraine", "United Kingdom", "Bosnia and Herzegovina", 
+        "Cyprus", "Turkey", "Greenland", "Faroe Islands"
     ],
     "North America": [
-        "Antigua and Barbuda", "Bahamas", "Barbados", "Belize", "Canada", "Costa Rica", "Cuba",
-        "Dominica", "Dominican Republic", "El Salvador", "Grenada", "Guatemala", "Haiti", "Honduras",
-        "Jamaica", "Mexico", "Nicaragua", "Panama", "Saint Kitts and Nevis", "Saint Lucia",
-        "Saint Vincent and the Grenadines", "Trinidad and Tobago", "United States"
+        "Canada", "Dominican Republic", "Guatemala", "Mexico", "United States", 
+        "Bahamas", "Cuba", "Panama", "Puerto Rico", "Bermuda", "Greenland"
     ],
     "Oceania": [
-        "Australia", "Fiji", "Kiribati", "Marshall Islands", "Micronesia", "Nauru", "New Zealand",
-        "Palau", "Papua New Guinea", "Samoa", "Solomon Islands", "Tonga", "Tuvalu", "Vanuatu"
+        "Australia", "New Zealand", "Fiji", "Papua New Guinea", "Solomon Islands", "Vanuatu"
     ],
     "South America": [
-        "Argentina", "Bolivia", "Brazil", "Chile", "Colombia", "Ecuador", "Guyana", "Paraguay",
-        "Peru", "Suriname", "Uruguay", "Venezuela"
+        "Argentina", "Bolivia", "Brazil", "Chile", "Colombia", "Ecuador", "Paraguay", 
+        "Peru", "Uruguay"
     ]
 }
 countries = list(set(itertools.chain.from_iterable(
     countries_per_continent.values())))
 
+country_to_center_coords = {
+    "Indonesia": (-2.4833826, 117.8902853),
+    "Egypt": (26.2540493, 29.2675469),
+    "Dominican Republic": (19.0974031, -70.3028026),
+    "Russia": (64.6863136, 97.7453061),
+    "Denmark": (55.670249, 10.3333283),
+    "Latvia": (56.8406494, 24.7537645),
+    "Hong Kong": (22.350627, 114.1849161),
+    "Brazil": (-10.3333333, -53.2),
+    "Turkey": (38.9597594, 34.9249653),
+    "Paraguay": (-23.3165935, -58.1693445),
+    "Nigeria": (9.6000359, 7.9999721),
+    "United Kingdom": (54.7023545, -3.2765753),
+    "Argentina": (-34.9964963, -64.9672817),
+    "United Arab Emirates": (24.0002488, 53.9994829),
+    "Estonia": (58.7523778, 25.3319078),
+    "Greenland": (69.6354163, -42.1736914),
+    "Canada": (61.0666922, -107.991707),
+    "Andorra": (42.5407167, 1.5732033),
+    "Czechia": (49.7439047, 15.3381061),
+    "Australia": (-24.7761086, 134.755),
+    "Azerbaijan": (40.3936294, 47.7872508),
+    "Cambodia": (12.5433216, 104.8144914),
+    "Peru": (-6.8699697, -75.0458515),
+    "Slovakia": (48.7411522, 19.4528646),
+    "Réunion": (-21.130737949999997, 55.536480112992315),
+    "France": (46.603354, 1.8883335),
+    "Israel": (30.8124247, 34.8594762),
+    "China": (35.000074, 104.999927),
+    "Ecuador": (-1.3397668, -79.3666965),
+    "Poland": (52.215933, 19.134422),
+    "Switzerland": (46.7985624, 8.2319736),
+    "Singapore": (1.357107, 103.8194992),
+    "Kenya": (1.4419683, 38.4313975),
+    "Bhutan": (27.549511, 90.5119273),
+    "Laos": (20.0171109, 103.378253),
+    "Vietnam": (15.9266657, 107.9650855),
+    "Puerto Rico": (18.2247706, -66.4858295),
+    "Germany": (51.1638175, 10.4478313),
+    "Tanzania": (-6.5247123, 35.7878438),
+    "Colombia": (4.099917, -72.9088133),
+    "Italy": (42.6384261, 12.674297),
+    "Bahamas": (24.7736546, -78.0000547),
+    "Panama": (8.559559, -81.1308434),
+    "Bulgaria": (42.6073975, 25.4856617),
+    "Solomon Islands": (-8.7053941, 159.1070693851845),
+    "Afghanistan": (33.7680065, 66.2385139),
+    "Tajikistan": (38.6281733, 70.8156541),
+    "Portugal": (39.6621648, -8.1353519),
+    "Tunisia": (36.8002068, 10.1857757),
+    "Bolivia": (-17.0568696, -64.9912286),
+    "Malaysia": (4.5693754, 102.2656823),
+    "Lithuania": (55.3500003, 23.7499997),
+    "Sweden": (59.6749712, 14.5208584),
+    "Belgium": (50.6402809, 4.6667145),
+    "Libya": (26.8234472, 18.1236723),
+    "Guatemala": (15.5855545, -90.345759),
+    "India": (22.3511148, 78.6677428),
+    "Sri Lanka": (7.5554942, 80.7137847),
+    "New Zealand": (-41.5000831, 172.8344077),
+    "Iceland": (64.9841821, -18.1059013),
+    "Somalia": (8.3676771, 49.083416),
+    "Croatia": (45.3658443, 15.6575209),
+    "Bosnia and Herzegovina": (44.3053476, 17.5961467),
+    "Greece": (38.9953683, 21.9877132),
+    "Rwanda": (-1.9646631, 30.0644358),
+    "Hungary": (47.1817585, 19.5060937),
+    "Eswatini": (-26.5624806, 31.3991317),
+    "Kyrgyzstan": (41.5089324, 74.724091),
+    "Bangladesh": (23.6943117, 90.344352),
+    "Morocco": (28.3347722, -10.371337908392647),
+    "Finland": (63.2467777, 25.9209164),
+    "Luxembourg": (49.6112768, 6.129799),
+    "North Macedonia": (41.6171214, 21.7168387),
+    "Uruguay": (-32.8755548, -56.0201525),
+    "Chile": (-31.7613365, -71.3187697),
+    "Spain": (39.3260685, -4.8379791),
+    "South Korea": (36.638392, 127.6961188),
+    "Botswana": (-23.1681782, 24.5928742),
+    "Uganda": (1.5333554, 32.2166578),
+    "Papua New Guinea": (-5.6816069, 144.2489081),
+    "Mali": (16.3700359, -2.2900239),
+    "Philippines": (12.7503486, 122.7312101),
+    "Norway": (64.5731537, 11.52803643954819),
+    "Thailand": (14.8971921, 100.83273),
+    "Mongolia": (46.8651082, 103.8347844),
+    "Japan": (36.5748441, 139.2394179),
+    "Montenegro": (42.7044223, 19.3957785),
+    "Austria": (47.59397, 14.12456),
+    "Taiwan": (23.6978, 120.9605),
+    "Netherlands": (52.2434979, 5.6343227),
+    "Ukraine": (49.4871968, 31.2718321),
+    "Fiji": (-18.1239696, 179.0122737),
+    "Ghana": (8.0300284, -1.0800271),
+    "Cuba": (23.0131338, -80.8328748),
+    "Nepal": (28.3780464, 83.9999901),
+    "Faroe Islands": (62.0448724, -7.0322972),
+    "Slovenia": (46.1199444, 14.8153333),
+    "Cyprus": (34.9174159, 32.889902651331866),
+    "Serbia": (44.024322850000004, 21.07657433209902),
+    "Madagascar": (-18.9249604, 46.4416422),
+    "Pakistan": (30.3308401, 71.247499),
+    "Syria": (34.6401861, 39.0494106),
+    "Iran": (32.6475314, 54.5643516),
+    "Ireland": (52.865196, -7.9794599),
+    "South Africa": (-28.8166236, 24.991639),
+    "Albania": (41.1529058, 20.1605717),
+    "Lesotho": (-29.6039267, 28.3350193),
+    "Romania": (45.9852129, 24.6859225),
+    "Palestine": (31.947351, 35.227163),
+    "Vanuatu": (-16.5255069, 168.1069154),
+    "Mexico": (19.4326296, -99.1331785),
+    "Jordan": (31.279862, 37.1297454),
+    "Djibouti": (11.8145966, 42.8453061),
+    "Senegal": (14.4750607, -14.4529612),
+    "Bermuda": (32.3040273, -64.7563086),
+    "United States": (39.7837304, -100.445882)
+}
+
 INTIAL_VERSUS_IMAGE = "versus_images/Europe_Germany_49.069183_10.319444_im2gps3k.jpg"
 INITAL_VERSUS_STATE = {
     "image": INTIAL_VERSUS_IMAGE,
@@ -95,8 +210,8 @@ def predict(input_img):
         pred_id = probs.argmax().cpu().item()
     continent_probs = {label: prob for label,
                        prob in zip(continents, probs.tolist()[0])}
-
-    predicted_continent_countries = countries_per_continent[continents[pred_id]]
+    model_continent = continents[pred_id]
+    predicted_continent_countries = countries_per_continent[model_continent]
     inputs = processor(text=[f"A photo from {
                        geo}." for geo in predicted_continent_countries], images=input_img, return_tensors="pt", padding=True)
     inputs = inputs.to(device)
@@ -104,57 +219,93 @@ def predict(input_img):
         outputs = country_model(**inputs)
         logits_per_image = outputs.logits_per_image
         probs = logits_per_image.softmax(dim=-1)
+        pred_id = probs.argmax().cpu().item()
+    model_country = predicted_continent_countries[pred_id]
     country_probs = {label: prob for label, prob in zip(
         predicted_continent_countries, probs.tolist()[0])}
-    return continent_probs, country_probs
-
+    
+    hash = hashlib.sha1(np.asarray(input_img).data.tobytes()).hexdigest()
+    metadata_block = gr.Accordion(visible=False)
+    metadata_map = None
+    if hash in EXAMPLE_METADATA.keys():
+        model_result = ""
+        if model_continent == EXAMPLE_METADATA[hash]['continent'] and model_country == EXAMPLE_METADATA[hash]['country']:
+            model_result = "The AI 🤖 correctly guessed continent and country  ✅ ✅."
+        elif model_continent == EXAMPLE_METADATA[hash]['continent']:
+            model_result = "The AI 🤖 only guessed the correct continent ❌ ✅."
+        elif model_country == EXAMPLE_METADATA[hash]['country'] and model_continent != EXAMPLE_METADATA[hash]['continent']:
+            model_result = "The AI 🤖 only guessed the correct country ✅ ❌."
+        else:
+            model_result = "The AI 🤖 failed to guess country and continent ❌ ❌."
+        metadata_block = gr.Accordion(visible=True, label=f"This photo was taken in {EXAMPLE_METADATA[hash]['country']}, {EXAMPLE_METADATA[hash]['continent']}.\n{model_result}")
+        metadata_map = make_versus_map(None, model_country, EXAMPLE_METADATA[hash])
+    return continent_probs, country_probs, metadata_block, metadata_map
 
 def make_versus_map(human_country, model_country, versus_state):
+    if human_country:
+        human_coordinates = country_to_center_coords[human_country]
+    else:
+        human_coordinates = (None, None)
+    model_coordinates = country_to_center_coords[model_country]
     fig = go.Figure()
-    fig.add_trace(go.Scattergeo(
+    fig.add_trace(go.Scattermapbox(
         lon=[versus_state["lon"]],
         lat=[versus_state["lat"]],
-        text=["📷"],
-        mode='text+markers',
+        text=[f"📷 Photo taken in {versus_state['country']}, {
+            versus_state['continent']}"],
+        mode='markers',
         hoverinfo='text',
-        hovertext=f"Photo taken in {versus_state['country']}, {
-            versus_state['continent']}",
-        marker=dict(size=14, color='#00B945'),
-        showlegend=False
-
+        marker=dict(size=14, color='#0C5DA5'),
+        showlegend=True,
+        name="📷 Photo Location"
     ))
     if human_country == model_country:
-        fig.add_trace(go.Scattergeo(
-            locations=[human_country],
-            locationmode='country names',
-            text=["🧑🤖"],
-            mode='text',
-            hoverinfo='location',
-            showlegend=False
-
+        fig.add_trace(go.Scattermapbox(
+            lat=[human_coordinates[0], model_coordinates[0]],
+            lon=[human_coordinates[1], model_coordinates[1]],
+            text=f"🧑 🤖 Human & AI guess {human_country}",
+            mode='markers',
+            hoverinfo='text',
+            marker=dict(size=14, color='#FF9500'),
+            showlegend=True,
+            name="🧑 🤖 Human & AI Guess"
         ))
     else:
-        fig.add_trace(go.Scattergeo(
-            locations=[human_country],
-            locationmode='country names',
-            text=["🧑"],
-            mode='text',
-            hoverinfo='location',
-            showlegend=False
-
+        if human_country:
+            fig.add_trace(go.Scattermapbox(
+                lat=[human_coordinates[0]],
+                lon=[human_coordinates[1]],
+                text=[f"🧑 Human guesses {human_country}"],
+                mode='markers',
+                hoverinfo='text',
+                marker=dict(size=14, color='#FF9500'),
+                showlegend=True,
+                name="🧑 Human Guess"
+            ))
+        fig.add_trace(go.Scattermapbox(
+            lat=[model_coordinates[0]],
+            lon=[model_coordinates[1]],
+            text=[f"🤖 AI guesses {model_country}"],
+            mode='markers',
+            hoverinfo='text',
+            marker=dict(size=14, color='#474747'),
+            showlegend=True,
+            name="🤖 AI Guess"
         ))
-        fig.add_trace(go.Scattergeo(
-            locations=[model_country],
-            locationmode='country names',
-            text=["🤖"],
-            mode='text',
-            hoverinfo='location',
-            showlegend=False
-
-        ))
-    fig.update_geos(
-        visible=True, resolution=110,
-        showcountries=True, countrycolor="grey", fitbounds="locations", projection_type="natural earth",
+     
+    fig.update_layout(
+        mapbox=dict(
+            style="carto-positron",
+            center=dict(lat=float(versus_state["lat"]), lon=float(versus_state["lon"])),
+            zoom=2
+        ),
+        margin={"r": 0, "t": 0, "l": 0, "b": 0},
+        legend=dict(
+            yanchor="bottom",
+            y=0.01,
+            xanchor="left",
+            x=0.01
+        )
     )
     return fig
 
@@ -174,11 +325,10 @@ def versus_mode_inputs(input_img, human_continent, human_country, versus_state):
         continent_result = "❌"
     human_result = f"The photo is from **{versus_state['country']}** {
         country_result} in **{versus_state['continent']}** {continent_result}"
-    human_score_update = f"+{
-        human_points} points" if human_points > 0 else "Wrong guess, try a new image."
+    human_score_update = f"+{human_points} points" if human_points > 0 else "0 Points..."
     versus_state['score']['HUMAN'] += human_points
 
-    continent_probs, country_probs = predict(input_img)
+    continent_probs, country_probs, _,_ = predict(input_img)
     model_country = max(country_probs, key=country_probs.get)
     model_continent = max(continent_probs, key=continent_probs.get)
     if model_country == versus_state["country"]:
@@ -191,8 +341,7 @@ def versus_mode_inputs(input_img, human_continent, human_country, versus_state):
         model_points += 1
     else:
         model_continent_result = "❌"
-    model_score_update = f"+{
-        model_points} points" if model_points > 0 else "The model was wrong, seems the world is not yet doomed."
+    model_score_update = f"+{model_points} points" if model_points > 0 else "0 Points... The model was completely wrong, it seems the world is not doomed yet."
     versus_state['score']['AI'] += model_points
 
     map = make_versus_map(human_country, model_country, versus_state)
@@ -230,8 +379,30 @@ def next_versus_image(versus_state):
     versus_state["image"] = versus_image
     return versus_image, versus_state, None, None
 
-demo = gr.Blocks()
+
+example_images = get_example_images("kerger-test-images")
+EXAMPLE_METADATA = {}
+for img_path in example_images:
+    hash = hashlib.sha1(np.asarray(Image.open(img_path)).data.tobytes()).hexdigest()
+    EXAMPLE_METADATA[hash] = {
+        "continent": img_path.split("/")[-1].split("_")[0],
+        "country": img_path.split("/")[-1].split("_")[1],
+        "lat": img_path.split("/")[-1].split("_")[2],
+        "lon": img_path.split("/")[-1].split("_")[3],
+    }
+
+demo = gr.Blocks(title="Thesis Demo")
 with demo:
+    gr.HTML("""
+<h1 style="text-align: center; margin-bottom: 1rem">Image Geolocation Thesis Demo</h1>
+            
+<h3> This Demo showcases the developed models and allows interacting with the optimized prototype.</h3>
+<p>Try the <b>"Image Geolocation Demo"</b> tab with your own images or with one of the examples. For all example image the ground truth is available and will be displayed together with the model predictions.</p>
+<p>In the  <b>"Versus Mode"</b> tab to play against the AI, guessing the country and continent where images where taken. Images in the versus mode are from the <a href="http://graphics.cs.cmu.edu/projects/im2gps/"><code>Im2GPS</code></a> and <a href="https://arxiv.org/abs/1705.04838"><code>Im2GPS3k</code></a> geolocation literature benchmarks. Can you beat the AI?
+
+""")
+    with gr.Accordion(label="The demo currently encompasses 116 countries from 6 continents 🌍", open=False):
+        gr.Code(json.dumps(countries_per_continent, indent=2, ensure_ascii=False), label="countries_per_continent.json", language="json", interactive=False)
     with gr.Tab("Image Geolocation Demo"):
         with gr.Row():
             with gr.Column():
@@ -243,12 +414,14 @@ with demo:
                 gr.Examples(examples=example_images,
                             inputs=image, examples_per_page=24)
             with gr.Column():
-                continents_label = gr.Label(label="Continents")
-                country_label = gr.Label(
-                    num_top_classes=5, label="Top countries")
-                # continents_label.select(predict_country, inputs=[image, continents_label], outputs=country_label)
+                with gr.Accordion(visible=False) as metadata_block:
+                    map = gr.Plot(label="Locations")
+                with gr.Group():
+                    continents_label = gr.Label(label="Continents")
+                    country_label = gr.Label(
+                        num_top_classes=5, label="Top countries")
     predict_btn.click(predict, inputs=image, outputs=[
-                      continents_label, country_label])
+                      continents_label, country_label, metadata_block, map])
 
     with gr.Tab("Versus Mode"):
         versus_state = gr.State(value=INITAL_VERSUS_STATE)
@@ -258,8 +431,7 @@ with demo:
                     INITAL_VERSUS_STATE["image"], interactive=False)
                 continent_selection = gr.Radio(
                     continents, label="Continents", info="Where was this image taken? (1 Point)")
-                country_selection = gr.Dropdown(countries, label="Countries", info="Can you guess the exact country? (2 Points)"
-                                                ),
+                country_selection = gr.Dropdown(countries, label="Countries", info="Can you guess the exact country? (2 Points)"),
                 with gr.Row():
                     next_img_btn = gr.Button("Try new image")
                     versus_btn = gr.Button("Submit guess")
@@ -268,13 +440,15 @@ with demo:
                 # with gr.Accordion("View Map", open=False):
                 map = gr.Plot(label="Locations")
                 with gr.Accordion("Full Model Output", open=False):
-                    continents_label = gr.Label(label="Continents")
-                    country_label = gr.Label(
-                        num_top_classes=5, label="Top countries")
-        next_img_btn.click(next_versus_image, inputs=[versus_state], outputs=[versus_image, versus_state, continent_selection, country_selection[0]])
+                    with gr.Group():
+                        continents_label = gr.Label(label="Continents")
+                        country_label = gr.Label(
+                            num_top_classes=5, label="Top countries")
+        next_img_btn.click(next_versus_image, inputs=[versus_state], outputs=[
+                           versus_image, versus_state, continent_selection, country_selection[0]])
         versus_btn.click(versus_mode_inputs, inputs=[versus_image, continent_selection, country_selection[0], versus_state], outputs=[
                          versus_output, continents_label, country_label, map, versus_state])
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(show_api=False)

countries_per_continent.json

@@ -0,0 +1,134 @@
+{
+    "Africa": [
+        "Botswana",
+        "Eswatini",
+        "Ghana",
+        "Kenya",
+        "Lesotho",
+        "Nigeria",
+        "Senegal",
+        "South Africa",
+        "Rwanda",
+        "Uganda",
+        "Tanzania",
+        "Madagascar",
+        "Djibouti",
+        "Mali",
+        "Libya",
+        "Morocco",
+        "Somalia",
+        "Tunisia",
+        "Egypt",
+        "Réunion"
+    ],
+    "Asia": [
+        "Bangladesh",
+        "Bhutan",
+        "Cambodia",
+        "China",
+        "India",
+        "Indonesia",
+        "Israel",
+        "Japan",
+        "Jordan",
+        "Kyrgyzstan",
+        "Laos",
+        "Malaysia",
+        "Mongolia",
+        "Nepal",
+        "Palestine",
+        "Philippines",
+        "Singapore",
+        "South Korea",
+        "Sri Lanka",
+        "Taiwan",
+        "Thailand",
+        "United Arab Emirates",
+        "Vietnam",
+        "Afghanistan",
+        "Azerbaijan",
+        "Cyprus",
+        "Iran",
+        "Syria",
+        "Tajikistan",
+        "Turkey",
+        "Russia",
+        "Pakistan",
+        "Hong Kong"
+    ],
+    "Europe": [
+        "Albania",
+        "Andorra",
+        "Austria",
+        "Belgium",
+        "Bulgaria",
+        "Croatia",
+        "Czechia",
+        "Denmark",
+        "Estonia",
+        "Finland",
+        "France",
+        "Germany",
+        "Greece",
+        "Hungary",
+        "Iceland",
+        "Ireland",
+        "Italy",
+        "Latvia",
+        "Lithuania",
+        "Luxembourg",
+        "Montenegro",
+        "Netherlands",
+        "North Macedonia",
+        "Norway",
+        "Poland",
+        "Portugal",
+        "Romania",
+        "Russia",
+        "Serbia",
+        "Slovakia",
+        "Slovenia",
+        "Spain",
+        "Sweden",
+        "Switzerland",
+        "Ukraine",
+        "United Kingdom",
+        "Bosnia and Herzegovina",
+        "Cyprus",
+        "Turkey",
+        "Greenland",
+        "Faroe Islands"
+    ],
+    "North America": [
+        "Canada",
+        "Dominican Republic",
+        "Guatemala",
+        "Mexico",
+        "United States",
+        "Bahamas",
+        "Cuba",
+        "Panama",
+        "Puerto Rico",
+        "Bermuda",
+        "Greenland"
+    ],
+    "Oceania": [
+        "Australia",
+        "New Zealand",
+        "Fiji",
+        "Papua New Guinea",
+        "Solomon Islands",
+        "Vanuatu"
+    ],
+    "South America": [
+        "Argentina",
+        "Bolivia",
+        "Brazil",
+        "Chile",
+        "Colombia",
+        "Ecuador",
+        "Paraguay",
+        "Peru",
+        "Uruguay"
+    ]
+}

requirements.txt

@@ -2,5 +2,5 @@ numpy
 gradio
 transformers
 torch
-itertools
-os
+plotly
+pillow