Refactor datetime logging to use timezone-aware timestamps and comment out unused transaction_date field in TransactionParser

app.py

@@ -7,7 +7,7 @@ import json
 from langgraph.graph import StateGraph, END
 from typing import TypedDict, Annotated
 import operator
-from langchain_core.messages import SystemMessage, HumanMessage, AnyMessage
+from langchain_core.messages import SystemMessage, AnyMessage
 # from langgraph.pregel import RetryPolicy
 from langgraph.types import RetryPolicy
 import json
@@ -19,6 +19,7 @@ from datetime import datetime
 from fastapi import HTTPException
 from langchain_google_genai import ChatGoogleGenerativeAI
 from opik.integrations.langchain import OpikTracer
+from pytz import timezone 
 
 # Load environment variables - for local development
 from dotenv import load_dotenv
@@ -39,7 +40,7 @@ class TransactionParser(BaseModel):
     dr_or_cr: str = Field(description="Identify if the transaction was debit (spent) or credit (received). Strictly choose one of the values - Debit or Credit")
     receiver: str = Field(description="The recipient of the transaction. Identify the Merchant Name from the message text.")
     category: str = Field(description="The category of the transaction. The category of the transaction is linked to the Merchant Name. Strictly choose from one the of values - Shopping,EMI,Education,Miscellaneous,Grocery,Utility,House Help,Travel,Transport,Food")
-    transaction_date: str = Field(description="Use today's date strictly in yyyy-mm-dd format.")
+    # transaction_date: str = Field(description="Use today's date strictly in yyyy-mm-dd format.")
     transaction_origin: str = Field(description="The origin of the transaction. Provide the card or account number as well.")
 
 class AgentState(TypedDict):
@@ -64,17 +65,17 @@ class Agent:
         self.model = model
 
     def classify_txn_type(self, state: AgentState) -> AgentState:
-        print(f"{datetime.now()}: Classifying transaction type...")
+        print(f"{datetime.now(timezone("Asia/Kolkata"))}: Classifying transaction type...")
         messages = state["messages"]
         if self.system:
             messages = [SystemMessage(content=self.system)] + messages
 
         message = self.model.invoke(messages)
-        print(f"{datetime.now()}: Classifying transaction type completed.")
+        print(f"{datetime.now(timezone("Asia/Kolkata"))}: Classifying transaction type completed.")
         return {"messages": [message]}
     
     def parse_message(self, state: AgentState) -> AgentState:
-        print(f"{datetime.now()}: Parsing transaction message...")
+        print(f"{datetime.now(timezone("Asia/Kolkata"))}: Parsing transaction message...")
         message = state["messages"][0]#.content
         system = """
         You are a helpful assistant skilled at parsing transaction messages and providing structured responses.
@@ -84,12 +85,12 @@ class Agent:
         prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])
         chain = prompt | self.model.with_structured_output(TransactionParser)
         result = chain.invoke({"topic": message})
-        print(f"{datetime.now()}: Parsing transaction message completed.")   
+        print(f"{datetime.now(timezone("Asia/Kolkata"))}: Parsing transaction message completed.")   
         
         return {"messages": [result]}
 
     def write_message(self, state: AgentState) -> AgentState:
-        print(f"{datetime.now()}: Writing transaction message to Google Sheets...")
+        print(f"{datetime.now(timezone("Asia/Kolkata"))}: Writing transaction message to Google Sheets...")
         result = state["messages"][-1]
 
         SCOPES = ('https://www.googleapis.com/auth/spreadsheets', 'https://www.googleapis.com/auth/drive')
@@ -106,10 +107,10 @@ class Agent:
         wk.update_value(f'B{nrows+2}', result.dr_or_cr)
         wk.update_value(f'C{nrows+2}', result.receiver)
         wk.update_value(f'D{nrows+2}', result.category)
-        wk.update_value(f'E{nrows+2}', datetime.now().strftime("%Y-%m-%d")) #result.transaction_date)
+        wk.update_value(f'E{nrows+2}', datetime.now(timezone("Asia/Kolkata")).strftime("%Y-%m-%d")) #result.transaction_date)
         wk.update_value(f'F{nrows+2}', result.transaction_origin)
         wk.update_value(f'G{nrows+2}', state["messages"][0])
-        print(f"{datetime.now()}: Writing transaction message to Google Sheets completed.")
+        print(f"{datetime.now(timezone("Asia/Kolkata"))}: Writing transaction message to Google Sheets completed.")
         return {"messages": ["Transaction Completed"]}
         
     def check_txn_and_decide(self, state: AgentState):
@@ -120,14 +121,7 @@ class Agent:
 
         return result == "Transaction"
     
-prompt = """You are a smart assistant adept at classifying different messages. \
-You will be penalized heavily for incorrect classification. \
-Your task is to classify the message into one of the following categories: \
-Transaction, OTP, Promotional, Scheduled, Non-Transaction. \
-Consider Salary Credit, Interest Credit, Redemtions as non-transactional messages.
-Output the classification in a structured format like below. \
-{"classification": "OTP"} \
-"""
+
 app = FastAPI()
 
 @app.get("/")
@@ -139,6 +133,15 @@ def write_message(data: dict, header: str = Header()):
     if header != HF_TOKEN:
         raise HTTPException(status_code=400, detail="Invalid header")
 
+    prompt = """You are a smart assistant adept at classifying different messages. \
+    You will be penalized heavily for incorrect classification. \
+    Your task is to classify the message into one of the following categories: \
+    Transaction, OTP, Promotional, Scheduled, Non-Transaction. \
+    Consider Salary Credit, Interest Credit, Redemptions as non-transactional messages.
+    Output the classification in a structured format like below. \
+    {"classification": "OTP"} \
+    """
+
     message = data['message']
     
     try: