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import re
from dataclasses import dataclass
from typing import List, Dict, Optional
import pandas as pd
import streamlit as st
import os
api_key = os.getenv("OPENAI_API_KEY")
print("API key loaded?", bool(api_key))
# from dotenv import load_dotenv
# load_dotenv()
# HuggingFace optional
try:
from transformers import pipeline
HF_AVAILABLE = True
except Exception:
HF_AVAILABLE = False
# OpenAI
try:
from openai import OpenAI
OPENAI_AVAILABLE = True
except Exception:
OPENAI_AVAILABLE = False
# Load environment variables
# load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
MODEL = os.getenv("MODEL", "gpt-3.5-turbo")
# Streamlit config
st.set_page_config(page_title="Personal Finance Chatbot", page_icon="💬", layout="wide")
@dataclass
class FinanceRecord:
date: str
description: str
amount: float
category: Optional[str] = None
class HuggingFaceProvider:
def __init__(self):
self.available = HF_AVAILABLE
self.name = "huggingface"
self.generator = None
if self.available:
try:
self.generator = pipeline("text2text-generation", model="google/flan-t5-small")
except Exception:
self.available = False
def ok(self):
return self.available and self.generator is not None
def generate(self, prompt: str, max_tokens: int = 256):
if not self.ok():
return "[HF provider unavailable]"
try:
result = self.generator(prompt, max_length=max_tokens, do_sample=True)
return result[0]['generated_text']
except Exception as e:
return f"[HF error] {e}"
class GraniteWatsonProvider:
def __init__(self):
self.name = "granite_watson"
def ok(self):
return True
def generate(self, prompt: str, max_tokens: int = 256):
return "[Granite/Watson] This is a placeholder response. Connect IBM SDK here."
class OpenAIProvider:
def __init__(self):
self.api_key = OPENAI_API_KEY
self.model = MODEL
self.client = None
if self.api_key and OPENAI_AVAILABLE:
try:
self.client = OpenAI(api_key=self.api_key)
except Exception:
self.client = None
self.name = "openai"
def ok(self):
return self.client is not None
def generate(self, prompt: str, max_tokens: int = 512):
if not self.client:
return "[OpenAI] API not configured. Please set OPENAI_API_KEY in your environment."
try:
resp = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": "You are a financial assistant."},
{"role": "user", "content": prompt},
],
max_tokens=max_tokens,
temperature=0.7,
)
return resp.choices[0].message.content.strip()
except Exception as e:
return f"[OpenAI error] {e}"
def categorize_with_ai(provider, description: str):
prompt = f"Categorize this financial transaction description into: Food, Rent, Utilities, Entertainment, Transport, Other.\nDescription: {description}\nCategory:"
return provider.generate(prompt)
def get_ai_suggestions(provider, records: List[FinanceRecord]):
df = pd.DataFrame([r.__dict__ for r in records])
prompt = (
"You are a financial advisor. Here are the user's transactions:\n"
f"{df.to_string(index=False)}\n\n"
"Provide insights and suggestions to improve savings and manage money better."
)
return provider.generate(prompt, max_tokens=400)
# Streamlit UI
st.title("💬 Personal Finance Chatbot")
st.write("Manage savings, taxes, and investments with AI guidance.")
provider_choice = st.selectbox("AI Provider", ["HuggingFace", "Granite/Watson", "OpenAI"], index=0)
hf_provider = HuggingFaceProvider()
granite_provider = GraniteWatsonProvider()
openai_provider = OpenAIProvider()
if provider_choice == "HuggingFace":
provider = hf_provider
elif provider_choice == "Granite/Watson":
provider = granite_provider
else:
provider = openai_provider
if "records" not in st.session_state:
st.session_state.records: List[FinanceRecord] = []
st.sidebar.header("Add Transaction")
date = st.sidebar.text_input("Date", "2025-08-30")
description = st.sidebar.text_input("Description", "")
amount = st.sidebar.number_input("Amount", 0.0, 1e9, step=100.0)
if st.sidebar.button("Add Record"):
record = FinanceRecord(date=date, description=description, amount=amount)
record.category = categorize_with_ai(provider, record.description)
st.session_state.records.append(record)
st.sidebar.success("Record added!")
if st.session_state.records:
st.subheader("Transaction Records")
df = pd.DataFrame([r.__dict__ for r in st.session_state.records])
st.dataframe(df)
st.subheader("AI Suggestions")
suggestions = get_ai_suggestions(provider, st.session_state.records)
st.write(suggestions)
else:
st.info("No records yet. Add transactions from the sidebar.")
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