Create dataset.py

dataset.py

@@ -0,0 +1,54 @@
+import datasets
+
+class TTIDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="Images generated using different TTI (Text-to-Image) models: FLUX, DreamShaper, Juggernaut XL, and Pony Diffusion V6 XL.",
+            features=datasets.Features({
+                "id": datasets.Value("int64"),
+                "createdAt": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "positivePrompt": datasets.Value("string"),
+                "negativePrompt": datasets.Value("string"),
+                "nsfw": datasets.Value("bool"),
+                "browsingLevel": datasets.Value("int32"),
+                "statsSummary": datasets.Value("int32"),
+                "usernameHash": datasets.Value("string"),
+                "Model": datasets.Value("string"),
+                "cfgScale": datasets.Value("float32"),
+                "sampler": datasets.Value("string"),
+                "Size": datasets.Value("string"),
+                "seed": datasets.Value("float64"),
+                "VAE": datasets.Value("string"),
+                "generationSystem": datasets.Value("string"),
+                "resourceIDs": datasets.Value("string"),  # stored as stringified list
+            }),
+            supervised_keys=None,
+            homepage="",
+            citation="",
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract({
+            "flux": "flux_images.csv",
+            "dreamshaper": "dreamshaper_images.csv",
+            "juggernaut": "juggernaut_images.csv",
+            "pony": "pony_diffusion_images.csv"
+        })
+
+        return [
+            datasets.SplitGenerator(name="flux", gen_kwargs={"filepath": data_files["flux"]}),
+            datasets.SplitGenerator(name="dreamshaper", gen_kwargs={"filepath": data_files["dreamshaper"]}),
+            datasets.SplitGenerator(name="juggernaut", gen_kwargs={"filepath": data_files["juggernaut"]}),
+            datasets.SplitGenerator(name="pony", gen_kwargs={"filepath": data_files["pony"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        import csv
+
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for idx, row in enumerate(reader):
+                yield idx, row