init

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+
+def single_inference():
+    pass
+
+def blended_inference():
+    pass
+
+TITLE = """MKG Analogy"""
+
+with gr.Blocks() as block:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(TITLE)
+
+        with gr.Tab("Single Analogical Reasoning"):
+            with gr.Row():
+                head_image = gr.Image(type='pil', label="Head Image")
+                head_ent = gr.Textbox(lines=1, label="Head Entity")
+                with gr.Column():
+                    tail_image = gr.Image(type='pil', label="Tail Image")
+                    tail_ent = gr.Textbox(lines=1, label="Tail Entity")
+                    with gr.Column():
+                        question_text = gr.Textbox(lines=1, label="Question Entity")
+                        question_ent = gr.Textbox(lines=1, label="Question Entity")
+            submit_btn = gr.Button("Submit")
+            output_text = gr.Textbox(label="Answer Name")
+
+            # examples=[['example01.jpg', MODELS[0], 'best'], ['example02.jpg', MODELS[0], 'best']]
+            # ex = gr.Examples(
+            #     examples=examples, 
+            #     fn=image_to_prompt, 
+            #     inputs=[input_image, input_model, input_mode], 
+            #     outputs=[output_text, share_button, community_icon, loading_icon], 
+            #     cache_examples=True, 
+            #     run_on_click=True
+            # )
+            # ex.dataset.headers = [""]
+
+        with gr.Tab("Blended Analogical Reasoning"):
+            pass
+        
+        # gr.HTML(ARTICLE)
+
+    # submit_btn.click(
+    #     fn=image_to_prompt, 
+    #     inputs=[input_image, input_model, input_mode], 
+    #     outputs=[output_text, share_button, community_icon, loading_icon]
+    # )
+    # share_button.click(None, [], [], _js=None)
+
+block.queue(max_size=64).launch(enable_queue=True)