feat: logging

app.py

@@ -1,3 +1,5 @@
+import uuid
+
 import streamlit as st
 from annotated_text import annotated_text
 import pymongo
@@ -11,6 +13,8 @@ from utils import (
 )
 from openai import OpenAI
 
+from utils_logging import send_log, make_log
+
 from const import (
     LIMITATIONS,
     GOAL,
@@ -51,6 +55,7 @@ if "login" not in st.session_state:
         res = col.find_one({"user_token": st.session_state.user_token})
         if res:
             st.session_state.login = True
+            st.session_state.id = str(uuid.uuid4())
         else:
             reject_login()
 
@@ -89,6 +94,11 @@ if clicked:
     job_ad_split, metrics = highlight_text(job_ad_field, st.session_state.data)
     indices = get_indices(metrics)
     openai_params = construct_params(job_ad_field)
+    st.session_state.openai_params = openai_params
+    st.session_state.job_ad = job_ad_field
+    st.session_state.metrics = metrics
+    st.session_state.job_ad_split = job_ad_split
+    st.session_state.indices = indices
 
     with col2:
         with st.container(border=True):
@@ -128,9 +138,14 @@ if clicked:
 
     with placeholder:
         with st.spinner("Performing analysis..."):
-            response = get_response(openai_params)
+            response, usage = get_response(openai_params)
+            st.session_state.response = response
+            st.session_state.usage = usage
         with st.container():
             cleaned_response = process_response(response)
+            st.session_state.cleaned_response = cleaned_response
+            send_log(make_log(st.session_state))
+            #print([choice.message.content for choice in st.session_state.response])
 
             minicols2[0].metric("Diversity Statement", cleaned_response["answer_4"], None)
             minicols2[1].metric("Generation Appeal", cleaned_response["answer_6"], None)

requirements.txt

@@ -3,3 +3,4 @@ openai
 pymongo
 st-annotated-text
 tiktoken
+uuid

utils.py

@@ -42,7 +42,7 @@ def get_response(params):
         st.error("We are sorry, we are unable to connect to our AI provider.")
         return []
 
-    return list(comp.choices)
+    return list(comp.choices), dict(comp.usage)
 
 
 def process_response(choices):

utils_logging.py

@@ -0,0 +1,28 @@
+import pymongo
+import streamlit as st
+import datetime
+
+def make_log(session_state):
+    log = {
+        "user_token": session_state.user_token,
+        "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "id": session_state.id,
+        "job_ad": session_state.job_ad,
+        "metrics": session_state.metrics,
+        "indices": session_state.indices,
+        "job_ad_split": session_state.job_ad_split,
+        "ai": {"params": session_state.openai_params,
+               "openai_response": [choice.message.content for choice in session_state.response],
+               "cleaned_response": session_state.cleaned_response,
+               "usage": session_state.usage},
+    }
+    return log
+
+
+def send_log(log):
+    try:
+        client = pymongo.MongoClient(st.secrets["mongo_login"])
+        col = client["Fairplay"]["logging"]
+        col.insert_one(log)
+    except Exception as e:
+        st.toast("Had an issue contacting our server...")