Update app.py

app.py

@@ -17,29 +17,26 @@ st.set_page_config(page_title="PSC Runtime",
 
 
 name = st.selectbox(
-    "",
+    "Choose a dataset",
     ["dl19", "dl20"],
     index=None,
     placeholder="Choose a dataset..."
 )
 
 model_name = st.selectbox(
-    "",
+    "Choose a model",
     ["gpt-3.5", "gpt-4"],
     index=None,
     placeholder="Choose a model..."
 )
 
-# "dl19"
 
 if name and model_name:
-    
     import torch
     # fn = f"dl19-gpt-3.5.pt"
     fn = f"{name}-{model_name}.pt"
     object = torch.load(fn)
-    
-    
+
     outputs = object[2]
     query2outputs = {}
     for output in outputs:
@@ -47,20 +44,19 @@ if name and model_name:
         assert len(all_queries) == 1
         query = list(all_queries)[0]
         query2outputs[query] = [x['hits'] for x in output]
-    
-    
+
     search_query = st.selectbox(
         "",
         sorted(query2outputs),
-        index=None,
-        placeholder="Choose a query from the list..."
+        # index=None,
+        # placeholder="Choose a query from the list..."
     )
     
     def preferences_from_hits(list_of_hits):
         docid2id = {}
         id2doc = {}
         preferences = []
-    
+
         for result in list_of_hits:
             for doc in result:
                 if doc["docid"] not in docid2id:
@@ -73,8 +69,8 @@ if name and model_name:
     
         #  = {v: k for k, v in docid2id.items()}
         return np.array(preferences), id2doc
-    
-    
+
+
     def load_qrels(name):
         import ir_datasets
         if name == "dl19":
@@ -89,8 +85,8 @@ if name and model_name:
         for qrel in dataset.qrels_iter():
             qrels[qrel.query_id][qrel.doc_id] = qrel.relevance
         return qrels
-    
-    
+
+
     def aggregate(list_of_hits):
         import numpy as np
         from permsc import KemenyOptimalAggregator, sum_kendall_tau, ranks_from_preferences
@@ -101,12 +97,12 @@ if name and model_name:
         # y_optimal = BordaRankAggregator().aggregate(preferences)
     
         return [id2doc[id] for id in y_optimal]
-    
+
     
     def write_ranking(search_results):
         # st.write(
         #     f'<p align=\"right\" style=\"color:grey;\"> Before aggregation for query [{search_query}] ms</p>', unsafe_allow_html=True)
-    
+
         qid = {result["qid"] for result in search_results}
         assert len(qid) == 1
         qid = list(qid)[0]
@@ -114,17 +110,17 @@ if name and model_name:
         for i, result in enumerate(search_results):
             result_id = result["docid"]
             contents = result["content"]
-    
+
             label = qrels[str(qid)].get(str(result_id), 0)
             if label == 3:
-                style = "style=\"color:blue;\""
+                style = "style=\"color:rgb(231, 95, 43);\""
             elif label == 2:
-                style = "style=\"color:green;\""
+                style = "style=\"color:rgb(238, 147, 49);\""
             elif label == 1:
-                style = "style=\"color:red;\""
+                style = "style=\"color:rgb(241, 177, 118);\""
             else:
                 style = "style=\"color:grey;\""
-    
+
             print(qid, result_id, label, style)
             # output = f'<div class="row"> <b>Rank</b>: {i+1} | <b>Document ID</b>: {result_id} | <b>Score</b>:{result_score:.2f}</div>'
             output = f'<div class="row" {style}> <b>Rank</b>: {i+1} | <b>Document ID</b>: {result_id}'