test

app.py

@@ -17,47 +17,44 @@ 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="Output image")
-#     ]
-
-#     iface = gr.Interface(
-#         image_text_zeroshot(image, text_list),
-#         inputs,
-#         "label",
-#         description="""...""",
-#         title="ImageBind",
-#     )
-
-#     iface.launch()
-
-def image_classifier(inp):
-    return {'cat': 0.3, 'dog': 0.7}
-
-demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
-demo.launch()
+def image_text_zeroshot(texts):
+    labels = [texts]
+    inputs = {
+        ModalityType.TEXT: data.load_and_transform_text(labels, 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 = "./assets/ICA-Logo.png" #{label: score for label, score in zip(labels, scores)}
+
+    return score_dict
+
+
+def main():
+
+    iface = gr.Interface(
+        fn= image_text_zeroshot(texts),
+        inputs = gr.inputs.Textbox(lines=1, label="texts"),
+        outputs = gr.inputs.Image(type="filepath", label="Output image"),
+        description="""...""",
+        title="ImageBind",
+    )
+
+    iface.launch()
+
+
+
+# def image_classifier(inp):
+#     return {'cat': 0.3, 'dog': 0.7}
+
+# demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
+# demo.launch()