Fix image encoder for Gradio 6.x: handle filepath/dict input types

app.py

@@ -0,0 +1,73 @@
+"""
+SigLIP 2 Text & Image Encoder -- HuggingFace Space
+Encodes text or image queries to 768-dim vectors for the Epstein photo search.
+
+Model: google/siglip2-base-patch16-224
+"""
+
+import gradio as gr
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer, AutoProcessor
+
+MODEL_NAME = "google/siglip2-base-patch16-224"
+
+print(f"Loading {MODEL_NAME}...")
+model = AutoModel.from_pretrained(MODEL_NAME).eval()
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+processor = AutoProcessor.from_pretrained(MODEL_NAME)
+print(f"Model loaded. Text hidden size: {model.config.text_config.hidden_size}")
+
+def encode(text: str) -> list:
+    inputs = tokenizer([text], return_tensors="pt", padding="max_length", max_length=64, truncation=True)
+    with torch.no_grad():
+        feats = model.text_model(**inputs).pooler_output
+        feats = F.normalize(feats, dim=-1)
+    return feats[0].tolist()
+
+def encode_image(image) -> list:
+    print(f"encode_image called with type: {type(image)}")
+    if image is None:
+        raise gr.Error("No image provided")
+    # Gradio 5+/6+ may pass a filepath string or dict instead of PIL Image
+    if isinstance(image, str):
+        image = Image.open(image).convert("RGB")
+    elif isinstance(image, dict):
+        # Gradio FileData dict: {"path": "/tmp/...", "url": "...", ...}
+        path = image.get("path") or image.get("url")
+        if path and path.startswith("data:"):
+            import base64, io
+            header, data = path.split(",", 1)
+            image = Image.open(io.BytesIO(base64.b64decode(data))).convert("RGB")
+        elif path:
+            image = Image.open(path).convert("RGB")
+        else:
+            raise gr.Error(f"Cannot parse image dict: {list(image.keys())}")
+    elif not isinstance(image, Image.Image):
+        raise gr.Error(f"Unexpected image type: {type(image)}")
+    print(f"Image size: {image.size}, mode: {image.mode}")
+    inputs = processor(images=[image], return_tensors="pt")
+    with torch.no_grad():
+        feats = model.get_image_features(pixel_values=inputs["pixel_values"])
+        if not isinstance(feats, torch.Tensor):
+            feats = feats.pooler_output
+        feats = F.normalize(feats, dim=-1)
+    return feats[0].tolist()
+
+with gr.Blocks(title="SigLIP 2 Encoder") as demo:
+    gr.Markdown("# SigLIP 2 Encoder\nEncodes text or images to 768-dim normalized vectors using google/siglip2-base-patch16-224")
+
+    with gr.Tab("Text"):
+        text_input = gr.Textbox(label="Text")
+        text_output = gr.JSON(label="Embedding (768-dim)")
+        text_btn = gr.Button("Encode Text")
+        text_btn.click(fn=encode, inputs=text_input, outputs=text_output, api_name="encode")
+
+    with gr.Tab("Image"):
+        image_input = gr.Image(type="pil", label="Image")
+        image_output = gr.JSON(label="Embedding (768-dim)")
+        image_btn = gr.Button("Encode Image")
+        image_btn.click(fn=encode_image, inputs=image_input, outputs=image_output, api_name="encode_image")
+
+demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch
+transformers>=4.49.0
+sentencepiece
+protobuf
+Pillow