Update app.py

app.py

@@ -1,4 +1,74 @@
 import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.title("TripletMix Demo")
+st.caption("For faster inference without waiting in queue, you may clone the space and run it yourself.")
+prog = st.progress(0.0, "Idle")
+tab_cls, tab_img, tab_text, tab_pc, tab_sd, tab_cap = st.tabs([
+    "Classification",
+    "Retrieval w/ Image",
+    "Retrieval w/ Text",
+    "Retrieval w/ 3D",
+    "Image Generation",
+    "Captioning",
+])
+
+
+def demo_classification():
+    with st.form("clsform"):
+        #load_data = misc_utils.input_3d_shape('cls')
+        cats = st.text_input("Custom Categories (64 max, separated with comma)")
+        cats = [a.strip() for a in cats.split(',')]
+        if len(cats) > 64:
+            st.error('Maximum 64 custom categories supported in the demo')
+            return
+        lvis_run = st.form_submit_button("Run Classification on LVIS Categories")
+        custom_run = st.form_submit_button("Run Classification on Custom Categories")
+        """
+        if lvis_run or auto_submit("clsauto"):
+            pc = load_data(prog)
+            col2 = misc_utils.render_pc(pc)
+            prog.progress(0.5, "Running Classification")
+            pred = classification.pred_lvis_sims(model_g14, pc)
+            with col2:
+                for i, (cat, sim) in zip(range(5), pred.items()):
+                    st.text(cat)
+                    st.caption("Similarity %.4f" % sim)
+            prog.progress(1.0, "Idle")
+        if custom_run:
+            pc = load_data(prog)
+            col2 = misc_utils.render_pc(pc)
+            prog.progress(0.5, "Computing Category Embeddings")
+            device = clip_model.device
+            tn = clip_prep(text=cats, return_tensors='pt', truncation=True, max_length=76, padding=True).to(device)
+            feats = clip_model.get_text_features(**tn).float().cpu()
+            prog.progress(0.5, "Running Classification")
+            pred = classification.pred_custom_sims(model_g14, pc, cats, feats)
+            with col2:
+                for i, (cat, sim) in zip(range(5), pred.items()):
+                    st.text(cat)
+                    st.caption("Similarity %.4f" % sim)
+            prog.progress(1.0, "Idle")
+        """
+    """
+    if image_examples(samples_index.classification, 3, example_text="Examples (Choose one of the following 3D shapes)"):
+        queue_auto_submit("clsauto")
+    """
+
+
+
+
+
+
+try:
+    with tab_cls:
+        demo_classification()
+    """
+    with tab_cap:
+        demo_captioning()
+    with tab_sd:
+        demo_pc2img()
+    demo_retrieval()
+    """
+except Exception:
+    import traceback
+    st.error(traceback.format_exc().replace("\n", "  \n"))