Update app.py

app.py

@@ -23,52 +23,34 @@ def demo_classification():
             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")
-    """
-
-
-
-
 
 
+def demo_captioning():
+    with st.form("capform"):
+        cond_scale = st.slider('Conditioning Scale', 0.0, 4.0, 2.0, 0.1, key='capcondscl')
+
+def demo_pc2img():
+    with st.form("sdform"):
+        prompt = st.text_input("Prompt (Optional)", key='sdtprompt')
+
+def demo_retrieval():
+    with tab_pc:
+        k = st.slider("Shapes to Retrieve", 1, 100, 16, key='rpc')
+    with tab_img:
+        with st.form("rimgform"):
+            k = st.slider("Shapes to Retrieve", 1, 100, 16, key='rimage')
+    with tab_text:
+        with st.form("rtextform"):
+            k = st.slider("Shapes to Retrieve", 1, 100, 16, key='rtext')
+            text = st.text_input("Input Text", key="inputrtext")
 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"))