Update app.py

app.py

@@ -4,18 +4,6 @@ import numpy as np
 import matplotlib.pyplot as plt
 from PIL import Image
 
-user_num = 7
-total_gpu = 32
-used_gpu = 22
-
-col1, col2, col3 = st.columns(3)
-col1.metric("Total GPU", f"{total_gpu} GB", f"{user_num} users are sharing now")
-col2.metric("Used", f"{used_gpu} GB")
-col3.metric("Percent", f"{100 * used_gpu/total_gpu} %",)
-
-chart_data = pd.DataFrame(np.random.randn(20, 3))
-
-st.area_chart(chart_data)
 
 
 sample_data = {
@@ -72,10 +60,54 @@ sample_data = {
         ], 
         'previous_hash':'5199cc3018287cf3f5fffbb0d1ae3f949256774a2347401818bdc93d29c379e8'
         },
+    {
+        'index': 5, 
+        'timestamp': 1701157843.582371, 
+        'transactions': [
+            {'id': 'Alice', 'kind': 'out', 'data': '16'}, 
+            {'id': 'bob', 'kind': 'out', 'data': '16'}, 
+            {'id': 'Alice', 'kind': 'inference', 'data': 'Hello?'}, 
+            {'id': 'Alice', 'kind': 'out', 'data': '16'}, 
+            {'id': 'bob', 'kind': 'out', 'data': '16'}
+        ],
+        'previous_hash':'5199cc3018287cf3f5fffbb0d1ae3f949256774a2347401818bdc93d29c379e8'
+        },
         
     ]
 } 
 
+
+user_num = len(sample_data['gpus'].items())
+total_gpu = sample_data['total']['total']
+used_gpu = sample_data['total']['used']
+
+name_list = []
+timestamp_gpu_data = []
+for block in sample_data['chain']:
+    for t in block['transactions']:
+        if t['kind'] == "add":
+            if not t['id'] in name_list:
+                name_list.append(t['id'])
+
+timestamp_gpu_data = [[0 for _ in range(len(name_list))]]
+for block in sample_data['chain']:
+    temp = timestamp_gpu_data[block['index']]
+    for t in block['transactions']:
+        if t['kind'] == "add":
+            temp[name_list.find(t['id'])] += int(t['data'])
+        elif t['kind'] == "out":
+            temp[name_list.find(t['id'])] = 0
+    
+col1, col2, col3 = st.columns(3)
+col1.metric("Total GPU", f"{total_gpu} GB", f"{user_num} users are sharing now")
+col2.metric("Used", f"{used_gpu} GB")
+col3.metric("Percent", f"{100 * used_gpu/total_gpu} %",)
+
+chart_data = pd.DataFrame(timestamp_gpu_data)
+
+st.area_chart(chart_data)
+
+
 class BubbleChart:
     def __init__(self, data, bubble_spacing=0, n_bins=5):