Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,158 +1,90 @@
-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.")
+import streamlit as st
+import pandas as pd
 
-provider_choice = st.selectbox("AI Provider", ["HuggingFace", "Granite/Watson", "OpenAI"], index=0)
+# ✅ OpenAI client
+from openai import OpenAI
 
-hf_provider = HuggingFaceProvider()
-granite_provider = GraniteWatsonProvider()
-openai_provider = OpenAIProvider()
+# -------------------------------------------------------------------
+# Load API Key securely
+# -------------------------------------------------------------------
+api_key = os.getenv("OPENAI_API_KEY")
 
-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)
+def mask_key(key: str) -> str:
+    """Return a masked version of the API key (safe for logs)."""
+    if not key:
+        return "❌ No Key Found"
+    return key[:6] + "..." + key[-4:]
+
+if not api_key:
+    st.error("❌ OPENAI_API_KEY is not set. Please add it in Hugging Face → Settings → Variables and secrets.")
+    st.stop()
 else:
-    st.info("No records yet. Add transactions from the sidebar.")
+    st.sidebar.success(f"🔑 API key loaded: {mask_key(api_key)}")
+
+# Initialize OpenAI client
+client = OpenAI(api_key=api_key)
+
+# -------------------------------------------------------------------
+# Streamlit App UI
+# -------------------------------------------------------------------
+st.set_page_config(page_title="💰 Personal Finance Chatbot", page_icon="💬", layout="wide")
+
+st.title("💬 Personal Finance Chatbot")
+st.write("Get intelligent guidance for **savings, taxes, and investments** using OpenAI.")
+
+# Store chat history
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+# -------------------------------------------------------------------
+# Finance transaction tracker
+# -------------------------------------------------------------------
+if "transactions" not in st.session_state:
+    st.session_state["transactions"] = []
+
+st.sidebar.header("📊 Add Transaction")
+amount = st.sidebar.number_input("Amount", min_value=0.0, format="%.2f")
+category = st.sidebar.selectbox("Category", ["Savings", "Investment", "Tax", "Expense"])
+if st.sidebar.button("Add Transaction"):
+    st.session_state["transactions"].append({"Amount": amount, "Category": category})
+    st.sidebar.success("✅ Transaction added!")
+
+if st.session_state["transactions"]:
+    df = pd.DataFrame(st.session_state["transactions"])
+    st.sidebar.subheader("💵 Transactions")
+    st.sidebar.dataframe(df)
+
+# -------------------------------------------------------------------
+# Chatbot interaction
+# -------------------------------------------------------------------
+st.subheader("💡 Ask me anything about your finances")
+
+user_input = st.text_input("Type your question here:")
+if st.button("Send") and user_input:
+    # Store user message
+    st.session_state["messages"].append({"role": "user", "content": user_input})
+
+    # Call OpenAI API
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=st.session_state["messages"],
+            max_tokens=500,
+            temperature=0.7,
+        )
+
+        bot_reply = response.choices[0].message.content
+        st.session_state["messages"].append({"role": "assistant", "content": bot_reply})
+
+    except Exception as e:
+        st.error(f"⚠️ Error: {str(e)}")
+
+# -------------------------------------------------------------------
+# Display conversation
+# -------------------------------------------------------------------
+for msg in st.session_state["messages"]:
+    if msg["role"] == "user":
+        st.markdown(f"**🧑 You:** {msg['content']}")
+    else:
+        st.markdown(f"**🤖 Bot:** {msg['content']}")