feat: added tab with top lists

app.py

@@ -2,9 +2,17 @@ import gradio as gr
 from utils.gradio_utils import *
 import os
 
+
+
 USER = os.getenv("USERNAME")
 PASS = os.getenv("PASSWORD")
 
+list_iface = gr.Interface(fn=compute_offer,
+            inputs=[gr.File(label="Upload CSV", type="file"),
+                    gr.Slider(1, 365, value=30, step=1, label="Days", info="Number of days to look back")
+                    ],
+            outputs="dataframe")
+
 domains_template = """tenderax.com
 reach-fortune.com
 joyful-healthy-hair.com
@@ -98,8 +106,6 @@ thedropgears.com"""
 
 
 with gr.Blocks() as aeon:
-
-    
     with gr.Tab(label="SWAKS"):
         with gr.Row():
             with gr.Column():
@@ -160,5 +166,7 @@ with gr.Blocks() as aeon:
                 mix_btn.click(fn=mix, inputs=[domains_raw, output_ips, num_of_ips],\
                         outputs=output_mix)
 
+    with gr.Tab(label="Top Lists"):
+        list_iface.render()
 
 aeon.launch(auth=(USER, PASS))

utils/gradio_utils.py

@@ -1,5 +1,6 @@
 import random
 import ipaddress
+import pandas as pd
 
 
 ### SWAKS ###
@@ -226,4 +227,34 @@ def generate_ips_per_subclass(ip_subclasses: str, num_of_ips: int) -> str:
 
                 # Assuming generate_ips_per_slash24 is the same as the previously discussed generate_ips function
                 ip_addresses.extend(generate_ips_per_slash24(ip_subclass_24, num_of_ips))
-    return "\n".join(ip_addresses)
+    return "\n".join(ip_addresses)
+
+
+### GENERATE TOP LISTS ###
+def compute_offer(csv_file, days_lookback):
+    cmp_list = ['MSP', 'HOM', 'NTU', 'HCK', 'DDS', 'MNP', 'PSC', 'DTL', 'GVS', 'ANP', 'WDR', 'BSG']
+
+    #raw_df = pd.read_csv('tools/data/30.08.2023.gabriel.sabau.campanii.csv', parse_dates=['Data'])
+    raw_df = pd.read_csv(csv_file.name, parse_dates=['Data'])
+
+    cols = ['Campanie', 'Oferta', 'Nume', 'Server', 'User',
+        'Lista Custom', 'Data', 'HClicks', 'Clicks', 'Unscribers', 'Openers',
+        'Click Open', 'Leads', 'CLike', 'Complains', 'Traps', 'Send']
+    comcast_df = raw_df[raw_df['Nume'].str.contains('|'.join(cmp_list))]
+    comcast_df = comcast_df[comcast_df['Domeniu'] == 'Comcast']
+    comcast_df = comcast_df[cols]
+    comcast_df['Click Open'] = comcast_df['Click Open'].str.replace('%', '').astype(float)
+    exclude_list = comcast_df[comcast_df['Data'] > (pd.Timestamp('now') - pd.Timedelta(days=days_lookback))]['Oferta'].unique()
+    comcast_df = comcast_df[~comcast_df['Oferta'].isin(exclude_list)]
+    comcast_df = comcast_df[comcast_df['Send'] > 350000]
+    comcast_df = comcast_df[comcast_df['Lista Custom'].str.contains('open')]
+
+    comcast_df.reset_index(drop=True, inplace=True)
+
+    final_df = comcast_df.groupby(["Oferta", "Nume"])\
+        .agg( N=('Oferta', 'count'), send_avg=('Send', 'mean'), CO=('Click Open', 'mean'))\
+        .sort_values(['CO', 'N'], ascending=False)
+    final_df['send_avg'] = final_df['send_avg'].round(2).astype(float)
+    final_df.reset_index(inplace=True)
+
+    return final_df