Add analytics

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import os
-from clients import CompanySpecificClient, UserSpecificClient,hooks,RengagmentEmail
+from clients import CompanySpecificClient, UserSpecificClient,hooks,RengagmentEmail,display_analytics
 
 st.set_page_config(page_title="SalesIntel",layout="wide")
 st.html("styles.html")
@@ -67,4 +67,5 @@ if __name__ == "__main__":
         unsafe_allow_html=True,
     )
     
-    main()
+    main()
+    display_analytics()

clients.py

@@ -8,6 +8,7 @@ import os
 import streamlit as st
 import pandas as pd
 import requests
+import datetime
 cold_host = os.getenv("backend_cold")
 hook_host = os.getenv("hook_host")  # Corrected here
 rengagment_host = os.getenv("rengagement_host")
@@ -350,4 +351,75 @@ def RengagmentEmail(email_receiver):
         else:
             st.error("Data transmission failed. Please verify that your file contains the required columns and try again. If the problem persists, please contact us.")
     
-    return None
+    return None
+
+
+def fetch_analytics_data(host, start_date=None, end_date=None):
+    """
+    Fetches analytics data from the specified host, processes it, and aggregates by week.
+    
+    Parameters:
+    host (str): The host URL to query.
+    start_date (datetime): The start date for filtering (inclusive).
+    end_date (datetime): The end date for filtering (inclusive).
+    
+    Returns:
+    pd.DataFrame: The processed and aggregated analytics data.
+    """
+    endpoint = f"{host}/analytics/"
+    response = requests.get(endpoint)
+    
+    if response.status_code != 200:
+        st.error(f"Failed to fetch analytics data from {host}")
+        return pd.DataFrame()
+    
+    data = response.json()
+    
+    # Process the fetched data
+    records = []
+    for email_receiver, timestamps in data.items():
+        for timestamp, count in timestamps.items():
+            records.append({
+                'email_receiver': email_receiver,
+                'timestamp': pd.to_datetime(timestamp),
+                'count': count
+            })
+    
+    df = pd.DataFrame(records)
+    
+    # Filter by date range if specified
+    if start_date:
+        df = df[df['timestamp'] >= pd.to_datetime(start_date)]
+    if end_date:
+        df = df[df['timestamp'] <= pd.to_datetime(end_date)]
+    
+    # Aggregate by week
+    df['week'] = df['timestamp'].dt.to_period('W').apply(lambda r: r.start_time)
+    df = df.groupby(['email_receiver', 'week']).agg({'count': 'sum'}).reset_index()
+    
+    return df
+
+def display_analytics():
+    """
+    Displays the analytics data with a time filter.
+    """
+    cold_host = os.getenv("backend_cold")
+    rengagement_host = os.getenv("rengagement_host")
+    hook_host = os.getenv("hook_host")
+    
+    # Time filter inputs
+    start_date = st.date_input("Start Date", value=pd.to_datetime("2024-10-01"))
+    end_date = st.date_input("End Date", value=pd.to_datetime("today"))
+    
+    cold_df = fetch_analytics_data(cold_host, start_date, end_date)
+    rengagement_df = fetch_analytics_data(rengagement_host, start_date, end_date)
+    hook_df = fetch_analytics_data(hook_host, start_date, end_date)
+    
+    st.html('<h4 class="hero-subtitle">Cold Emails</h4>')
+    st.dataframe(cold_df)
+    
+    st.html('<h4 class="hero-subtitle">Re-engagement Emails</h4>')
+    st.dataframe(rengagement_df)
+    
+    st.html('<h4 class="hero-subtitle">Re-engagement Hooks "Hook2Lead"</h4>')
+    st.dataframe(hook_df)