Initial Commit

app.py

@@ -1,7 +1,121 @@
 from fastapi import FastAPI
+import os
 
 app = FastAPI()
 
-@app.get("/")
+@app.post("/")
 def greet_json():
-    return {"Hello": "World!"}
+    return {"Hello": "World!"}
+
+GOOGLESHEETS_CREDENTIALS = os.getenv("GOOGLESHEETS_CREDENTIALS")
+
+# from langchain_core.prompts import ChatPromptTemplate
+# from langchain_groq import ChatGroq
+# from pydantic import BaseModel, Field
+# import pygsheets
+# 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_ollama import ChatOllama
+# import groq
+# from tqdm import tqdm
+# from opik.integrations.langchain import OpikTracer
+# from langgraph.pregel import RetryPolicy
+
+# MODEL = "llama3-70b-8192" # "meta-llama/llama-4-scout-17b-16e-instruct" #
+
+# class TransactionParser(BaseModel):
+#     """This Pydantic class is used to parse the transaction message."""
+
+#     amount: str = Field(description="The amount of the transaction in decimal format. If the transaction is a credit or a reversal, then include negative sign. DO not insert currency.", example="123.45")
+#     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 value.")
+#     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")
+#     transaction_date: str = Field(description="The date of the transaction in yyyy-mm-dd format. If the year is not provided then use current year.")
+#     transaction_origin: str = Field(description="The origin of the transaction. Provide the card or account number as well.")
+
+# class AgentState(TypedDict):
+#     messages: Annotated[list[AnyMessage], operator.add]
+
+# class Agent:
+#     def __init__(self, model, system=""):
+#         self.system = system
+#         graph = StateGraph(AgentState)
+#         graph.add_node("classify_txn_type", self.classify_txn_type, retry=RetryPolicy(retry_on=[groq.BadRequestError], max_attempts=2))
+#         graph.add_node("parse_message", self.parse_message, retry=RetryPolicy(retry_on=[groq.BadRequestError], max_attempts=2))
+#         graph.add_node("write_message", self.write_message)
+#         graph.add_conditional_edges(
+#             "classify_txn_type",
+#             self.check_txn_and_decide,
+#             {True: "parse_message", False: END}
+#             )
+#         graph.add_edge("parse_message", "write_message")
+#         graph.add_edge("write_message", END)
+#         graph.set_entry_point("classify_txn_type")
+#         self.graph = graph.compile()
+#         self.model = model
+
+#     def classify_txn_type(self, state: AgentState) -> AgentState:
+#         messages = state["messages"]
+#         if self.system:
+#             messages = [SystemMessage(content=self.system)] + messages
+
+#         message = self.model.invoke(messages)
+#         return {"messages": [message]}
+    
+#     def parse_message(self, state: AgentState) -> AgentState:
+#         message = state["messages"][0].content
+#         system = """
+#         You are a helpful assistant skilled at parsing transaction messages and providing structured responses.
+#         """
+#         human = "Categorize the transaction message and provide the output in a structed format: {topic}"
+
+#         prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])
+#         chain = prompt | model.with_structured_output(TransactionParser)
+#         result = chain.invoke({"topic": message})
+        
+#         return {"messages": [result]}
+
+#     def write_message(self, state: AgentState) -> AgentState:
+#         result = state["messages"][-1]
+#         client = pygsheets.authorize(service_account_file="serviceaccount.json")
+#         worksheet = client.open_by_url("https://docs.google.com/spreadsheets/d/1t4bOM4fULdaVsjDDnqEG1g8Zey6M00UuFhTZC03_4xo")
+#         wk = worksheet[0]
+#         # Get number of rows in the worksheet
+#         df = wk.get_as_df(start='A1', end='G999')
+#         nrows = df.shape[0]
+#         wk.update_value(f'A{nrows+2}', result.amount)
+#         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}', result.transaction_date)
+#         wk.update_value(f'F{nrows+2}', result.transaction_origin)
+#         wk.update_value(f'G{nrows+2}', state["messages"][0].content)
+#         return {"messages": ["Transaction Completed"]}
+        
+#     def check_txn_and_decide(self, state: AgentState):
+#         try:
+#             result = json.loads(state['messages'][-1].content)['classification']
+#         except json.JSONDecodeError:
+#             result = state['messages'][-1].content.strip()
+
+#         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. \
+# Output the classification in a structured format like below. \
+# {"classification": "OTP"} \
+# """
+
+# model = ChatGroq(temperature=1, groq_api_key=GROQ_API_KEY, model_name=MODEL)
+# # model = ChatOllama(model="gemma3:4b", temperature=1, callback=OpikTracer())
+
+# transaction_bot = Agent(model, system=prompt)
+
+# for message in tqdm(messages):
+#     message = [HumanMessage(content=message)]
+#     result = transaction_bot.graph.invoke({"messages": message}, config={"callbacks" : [OpikTracer(graph=transaction_bot.graph.get_graph(xray=True))]})

requirements.txt

@@ -1,2 +1,3 @@
+os
 fastapi
 uvicorn[standard]