test

app.py

@@ -17,47 +17,21 @@ model = imagebind_model.imagebind_huge(pretrained=True)
 model.eval()
 model.to(device)
 
-def image_text_zeroshot(text):
-    # labels = [text]
-    # 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,
-        inputs="text",
-        outputs="file",
-        live=True,
-        capture_session=True,
-        title="Texto para Imagem",
-        description="Digite um texto e obtenha uma imagem com o texto.",
-        allow_flagging=False,
-    )
-
-    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 generate_image(text):
+    return "./assets/ICA-Logo.png"
+
+# Interface do Gradio
+iface = gr.Interface(
+    fn=generate_image,
+    inputs="text",
+    outputs="file",
+    live=True,
+    capture_session=True,
+    title="Texto para Imagem",
+    description="Digite um texto e obtenha uma imagem com o texto.",
+    allow_flagging=False,
+)
+
+# Executa o servidor Gradio
+iface.launch()

assets/image_features_norm.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e03967fc728ee9d3c0ded40f39aaac85360cd7acf6efc18f722b47b7d7187fd9
+size 33175