Create app.py

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+import data
+import torch
+import gradio as gr
+from models import imagebind_model
+from models.imagebind_model import ModalityType
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model = imagebind_model.imagebind_huge(pretrained=True)
+model.eval()
+model.to(device)
+
+def image_text_zeroshot(image, text_list):
+    image_paths = [image]
+    labels = [label.strip(" ") for label in text_list.strip(" ").split("|")]
+    inputs = {
+        ModalityType.TEXT: data.load_and_transform_text(labels, device),
+        ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
+    }
+
+    with torch.no_grad():
+        embeddings = model(inputs)
+
+    scores = (
+        torch.softmax(
+            embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1
+        )
+        .squeeze(0)
+        .tolist()
+    )
+
+    score_dict = {label: score for label, score in zip(labels, scores)}
+
+    return score_dict
+
+def main():
+    inputs = [
+        gr.inputs.Textbox(lines=1, label="texts"),
+        gr.inputs.Image(type="filepath", label="Input image")
+    ]
+
+    iface = gr.Interface(
+        image_text_zeroshot(image, text_list),
+        inputs,
+        "label",
+        description="""...""",
+        title="ImageBind",
+    )
+
+    iface.launch()