Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,90 +1,248 @@
-import os
+# personal_finance_chatbot.py
 import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+import json
+from datetime import datetime
+import random
 import pandas as pd
-
-# ✅ OpenAI client
-from openai import OpenAI
-
-# -------------------------------------------------------------------
-# Load API Key securely
-# -------------------------------------------------------------------
-api_key = os.getenv("OPENAI_API_KEY")
-
-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.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,
+import numpy as np
+
+# Configuration
+MODEL_NAME = "ibm/granite-7b-base"  # Using IBM Granite model via HuggingFace
+USER_TYPES = ["student", "professional"]
+FINANCE_CATEGORIES = ["savings", "taxes", "investments", "budget", "spending"]
+
+# Initialize NLP pipeline
+@st.cache_resource
+def load_model():
+    """Load and cache the language model"""
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+    return pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+
+# User profile management
+class UserProfile:
+    def __init__(self, user_type, financial_goals=None, income=0, expenses=None):
+        self.user_type = user_type
+        self.financial_goals = financial_goals or []
+        self.income = income
+        self.expenses = expenses or {}
+        self.transaction_history = []
+
+    def add_transaction(self, amount, category, description=""):
+        """Record a financial transaction"""
+        self.transaction_history.append({
+            "date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            "amount": amount,
+            "category": category,
+            "description": description
+        })
+
+    def get_budget_summary(self):
+        """Generate a budget summary"""
+        total_expenses = sum(t["amount"] for t in self.transaction_history
+                           if t["amount"] < 0)
+        total_income = sum(t["amount"] for t in self.transaction_history
+                         if t["amount"] > 0)
+
+        return {
+            "total_income": total_income,
+            "total_expenses": abs(total_expenses),
+            "net_savings": total_income + total_expenses,
+            "category_breakdown": self._get_category_breakdown()
+        }
+
+    def _get_category_breakdown(self):
+        """Get expense breakdown by category"""
+        breakdown = {}
+        for t in self.transaction_history:
+            if t["amount"] < 0:
+                cat = t["category"]
+                breakdown[cat] = breakdown.get(cat, 0) + abs(t["amount"])
+        return breakdown
+
+# Chatbot core functionality
+class FinanceChatbot:
+    def __init__(self):
+        self.nlp = load_model()
+        self.user_profiles = {}
+        self.current_user = None
+
+    def set_user(self, user_id, user_type):
+        """Initialize or switch user profile"""
+        if user_id not in self.user_profiles:
+            self.user_profiles[user_id] = UserProfile(user_type=user_type)
+        self.current_user = user_id
+
+    def generate_response(self, query):
+        """Generate context-aware response to user query"""
+        if not self.current_user:
+            return "Please identify yourself first. Are you a student or professional?"
+
+        profile = self.user_profiles[self.current_user]
+        context = self._build_context(profile)
+
+        # Adjust tone based on user type
+        tone_instruction = (
+            "Use simple, encouraging language suitable for a student."
+            if profile.user_type == "student" else
+            "Use professional, concise language suitable for a financial professional."
         )
 
-        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']}")
+        prompt = f"""
+        You are a helpful financial assistant. {tone_instruction}
+        User profile context: {context}
+        Current query: {query}
+
+        Provide a helpful response that:
+        1. Directly answers the financial question
+        2. Offers 1-2 specific actionable suggestions when appropriate
+        3. Maintains a {profile.user_type}-appropriate tone
+        4. Is concise (3 sentences or less unless more detail is requested)
+        """
+
+        try:
+            result = self.nlp(prompt, max_length=200, num_return_sequences=1)
+            response = result[0]['generated_text'].strip()
+
+            # Post-process to extract clean response
+            if "Response:" in response:
+                response = response.split("Response:")[-1].strip()
+            return response
+        except Exception as e:
+            return f"Sorry, I encountered an error processing your request. {str(e)}"
+
+    def _build_context(self, profile):
+        """Build context string from user profile"""
+        budget = profile.get_budget_summary()
+        return json.dumps({
+            "user_type": profile.user_type,
+            "income": profile.income,
+            "net_savings": budget["net_savings"],
+            "top_expenses": sorted(budget["category_breakdown"].items(),
+                                 key=lambda x: x[1], reverse=True)[:3],
+            "recent_transactions": profile.transaction_history[-3:] if profile.transaction_history else []
+        })
+
+    def analyze_spending(self):
+        """Generate spending insights"""
+        if not self.current_user:
+            return "No user profile selected"
+
+        profile = self.user_profiles[self.current_user]
+        budget = profile.get_budget_summary()
+
+        if not budget["category_breakdown"]:
+            return "No spending data available for analysis"
+
+        prompt = f"""
+        Analyze this spending data and provide insights:
+        {json.dumps(budget['category_breakdown'])}
+
+        User type: {profile.user_type}
+        Income: {profile.income}
+
+        Provide:
+        1. 1 key insight about spending patterns
+        2. 1 specific suggestion for optimization
+        3. Formatted for {profile.user_type} audience
+        """
+
+        try:
+            result = self.nlp(prompt, max_length=150)
+            return result[0]['generated_text'].strip()
+        except Exception as e:
+            return f"Error generating insights: {str(e)}"
+
+# Streamlit UI
+def main():
+    st.set_page_config(page_title="Personal Finance Chatbot", layout="wide")
+
+    # Initialize chatbot
+    if 'chatbot' not in st.session_state:
+        st.session_state.chatbot = FinanceChatbot()
+    if 'user_id' not in st.session_state:
+        st.session_state.user_id = None
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+
+    # Sidebar for user setup
+    with st.sidebar:
+        st.title("User Profile")
+        user_id = st.text_input("Your ID (any name)", value=st.session_state.get('user_id', ''))
+        user_type = st.selectbox("I am a:", USER_TYPES)
+
+        if st.button("Save Profile"):
+            st.session_state.user_id = user_id
+            st.session_state.chatbot.set_user(user_id, user_type)
+            st.success(f"Profile saved as {user_type}")
+
+        st.divider()
+        st.subheader("Quick Actions")
+
+        if st.session_state.user_id:
+            col1, col2 = st.columns(2)
+            with col1:
+                if st.button("Budget Summary"):
+                    profile = st.session_state.chatbot.user_profiles[st.session_state.user_id]
+                    summary = profile.get_budget_summary()
+                    st.session_state.messages.append(
+                        {"role": "assistant", "content": f"### Your Budget Summary\n\n"
+                                                        f"- **Income**: ${summary['total_income']:.2f}\n"
+                                                        f"- **Expenses**: ${summary['total_expenses']:.2f}\n"
+                                                        f"- **Net Savings**: ${summary['net_savings']:.2f}\n\n"
+                                                        f"Top Expenses: {', '.join([f'{k}(${v:.2f})' for k,v in summary['category_breakdown'].items()])}"
+                    )
+            with col2:
+                if st.button("Spending Insights"):
+                    insights = st.session_state.chatbot.analyze_spending()
+                    st.session_state.messages.append(
+                        {"role": "assistant", "content": f"### Spending Insights\n\n{insights}"}
+                    )
+
+    # Main chat interface
+    st.title("💰 Personal Finance Chatbot")
+    st.write("Ask me about savings, taxes, investments, or budgeting!")
+
+    # Display chat messages
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Chat input
+    if prompt := st.chat_input("What would you like to know about your finances?"):
+        if not st.session_state.user_id:
+            st.error("Please set up your profile first!")
+        else:
+            # Add user message to chat history
+            st.session_state.messages.append({"role": "user", "content": prompt})
+            with st.chat_message("user"):
+                st.markdown(prompt)
+
+            # Generate and display assistant response
+            with st.spinner("Thinking..."):
+                response = st.session_state.chatbot.generate_response(prompt)
+                with st.chat_message("assistant"):
+                    st.markdown(response)
+                st.session_state.messages.append({"role": "assistant", "content": response})
+
+    # Sample transactions for demo
+    if st.session_state.user_id and st.checkbox("Load sample data (demo only)"):
+        profile = st.session_state.chatbot.user_profiles[st.session_state.user_id]
+        sample_data = [
+            (1200, "income", "Monthly salary"),
+            (-400, "housing", "Rent payment"),
+            (-200, "food", "Groceries"),
+            (-150, "transportation", "Public transport"),
+            (-100, "entertainment", "Movies"),
+            (50, "investments", "Stock purchase"),
+            (-80, "utilities", "Electricity bill")
+        ]
+
+        for amount, category, desc in sample_data:
+            profile.add_transaction(amount, category, desc)
+
+        st.success("Loaded sample transactions! Try asking about your budget or spending.")
+
+if __name__ == "__main__":
+    main()