first commit

.vscode/launch.json

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+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "envFile": "${workspaceFolder}/.env",
+            "python": "${workspaceFolder}/../data-viber/.venv/bin/python"
+        }
+    ]
+}

app.py

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+import os
+import io
+import random
+
+import requests
+from PIL import Image
+from data_viber import AnnotatorInterFace
+
+HF_TOKEN = os.environ["HF_TOKEN"]
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
+DATASET_SERVER_URL = "https://datasets-server.huggingface.co"
+DATASET_NAME = "poloclub%2Fdiffusiondb&config=2m_random_1k&split=train"
+MODEL_URL = (
+    "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
+)
+
+
+def retrieve_sample(idx):
+    api_url = f"{DATASET_SERVER_URL}/rows?dataset={DATASET_NAME}&offset={idx}&length=1"
+    response = requests.get(api_url, headers=HEADERS)
+    data = response.json()
+    img_url = data["rows"][0]["row"]["image"]["src"]
+    prompt = data["rows"][0]["row"]["prompt"]
+    return img_url, prompt
+
+
+def get_rows():
+    api_url = f"{DATASET_SERVER_URL}/size?dataset={DATASET_NAME}"
+    response = requests.get(api_url, headers=HEADERS)
+    num_rows = response.json()["size"]["config"]["num_rows"]
+    return num_rows
+
+
+def generate_response(prompt):
+    payload = {
+        "inputs": prompt,
+    }
+    response = requests.post(MODEL_URL, headers=HEADERS, json=payload)
+    image = Image.open(io.BytesIO(response.content))
+    return image
+
+
+def next_input(_prompt, _completion_a, _completion_b):
+    random_idx = random.randint(0, get_rows()) - 1
+    img_url, prompt = retrieve_sample(random_idx)
+    generated_image = generate_response(prompt)
+    return (prompt, img_url, generated_image)
+
+if __name__ == "__main__":
+    interface = AnnotatorInterFace.for_image_generation_preference(
+        fn=next_input,
+        dataset_name=None,
+    )
+    interface.launch()

requirements.txt

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+gradio[oauth]>=4.3,<5
+datasets>=2,<3
+sentence-transformers>=3
+optimum[onnxruntime]>=1.21.3
+tabulate>=0.9.0
+diffusers>=0.30.0
+transformers>=4.43.4
+ipykernel>=6.29.5
+umap-learn>=0.5,<1
+plotly>=5,<6
+dash>=2.11,<3
+dash-bootstrap-components>=1.6.0
+pre-commit>=3.8.0
+ruff>=0.5,<1
+pytest>=8,<9
+black>=24,<25
+openpyxl>=3,<4
+requests
+pillow