Update app.py

app.py

@@ -1,13 +1,72 @@
 import gradio as gr
+import csv
+import os
+from datetime import datetime
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import time
 
 active_count = 0
+model = None
 
-def predict(input_data):
-    global active_count
+
+def log_data(count):
+    file_exists = os.path.isfile("usage.csv")
+    
+    with open("usage.csv", "a", newline="") as f:
+        writer = csv.writer(f)
+        
+        if not file_exists:
+            writer.writerow(["time", "active_users"])
+        
+        writer.writerow([datetime.now(), count])
+
+
+def train_model():
+    global model
+    
+    if not os.path.exists("usage.csv"):
+        return None
+    
+    data = pd.read_csv("usage.csv")
+    
+    if len(data) < 2:
+        return None
+    
+    data['time'] = pd.to_datetime(data['time'])
+    data['time'] = data['time'].astype(int) // 10**9
+    
+    X = data[['time']]
+    y = data['active_users']
+    
+    model = LinearRegression()
+    model.fit(X, y)
+
+def predict(input_text):
+    global active_count, model
+    
+    # increase count
     active_count += 1
     
-    # dummy prediction
-    return f"Active Users (estimated): {active_count}"
+    # log data
+    log_data(active_count)
+    
+    # train model
+    train_model()
+    
+    # prediction
+    if model:
+        current_time = int(time.time())
+        pred = model.predict([[current_time]])[0]
+        return f"Estimated Active Users: {int(pred)}"
+    else:
+        return f"Current Active Users (approx): {active_count}"
+
+iface = gr.Interface(
+    fn=predict,
+    inputs="text",
+    outputs="text",
+    title="Active Users Predictor"
+)
 
-iface = gr.Interface(fn=predict, inputs="text", outputs="text")
-iface.launch()
+iface.launch(share = True)