Update src/streamlit_app.py

src/streamlit_app.py

@@ -6,17 +6,21 @@ from typing import List, Dict, Optional
 import pandas as pd
 import streamlit as st
 
-# HF transformers optional import
+# Hugging Face (optional)
 try:
     from transformers import pipeline
     HF_AVAILABLE = True
 except Exception:
     HF_AVAILABLE = False
 
-# Streamlit config
+# -------------------------
+# Streamlit Config
+# -------------------------
 st.set_page_config(page_title="Personal Finance Chatbot", page_icon="💬", layout="wide")
 
-# Session keys robust initialization
+# -------------------------
+# Session State Init
+# -------------------------
 if "chat_history" not in st.session_state:
     st.session_state["chat_history"] = []
 if "providers" not in st.session_state:
@@ -26,13 +30,34 @@ if "provider_inited" not in st.session_state:
 if "provider_name" not in st.session_state:
     st.session_state["provider_name"] = "huggingface"
 
+# -------------------------
+# Categories & Intent Patterns
+# -------------------------
+CATEGORIES = {
+    "food": ["grocery", "restaurant", "dining"],
+    "transport": ["bus", "metro", "uber", "ola", "taxi"],
+    "entertainment": ["movie", "netflix", "spotify"],
+    "other": []
+}
 
+INTENT_PATTERNS = {
+    "savings": r"\bsav(e|ings|ing)\b",
+    "tax": r"\btax(es)?\b",
+    "invest": r"\binvest(ment|ing)?\b",
+    "budget": r"\bbudget(ing)?\b"
+}
+
+# -------------------------
+# AI Providers
+# -------------------------
 class AIProvider:
     def __init__(self):
         self.name = "base"
+
     def generate(self, prompt: str, max_tokens: int = 512) -> str:
         raise NotImplementedError
 
+
 class HuggingFaceProvider(AIProvider):
     def __init__(self):
         super().__init__()
@@ -44,16 +69,20 @@ class HuggingFaceProvider(AIProvider):
             except Exception as e:
                 st.warning(f"HuggingFace pipeline failed to load: {e}")
         else:
-            st.info("Transformers not installed; responses will be rule‑based only.")
+            st.info("Transformers not installed; responses will be rule-based only.")
+
     def generate(self, prompt: str, max_tokens: int = 512) -> str:
         if self.gen is None:
             return (
                 "[Rule-based fallback]\n"
                 + prompt[:1000]
-                + "\n\n(Summarized suggestion) Consider tracking expenses, setting goals, building an emergency fund, and using diversified, low-cost index funds aligned with your risk tolerance.)"
+                + "\n\n(Summarized suggestion) Consider tracking expenses, setting goals, "
+                  "building an emergency fund, and using diversified, low-cost index funds aligned "
+                  "with your risk tolerance.)"
             )
         out = self.gen(prompt, max_length=min(1024, max_tokens), do_sample=False)
-        return out["generated_text"].strip()
+        return out[0]["generated_text"].strip()
+
 
 class IBMGraniteWatsonProvider(AIProvider):
     def __init__(self, watson_api_key: Optional[str], watson_url: Optional[str], granite_key: Optional[str]):
@@ -63,18 +92,19 @@ class IBMGraniteWatsonProvider(AIProvider):
         self.watson_api_key = watson_api_key
         self.watson_url = watson_url
         self.granite_key = granite_key
+
     def generate(self, prompt: str, max_tokens: int = 512) -> str:
         if not self.ok:
             return "[IBM placeholder] Missing credentials — falling back text.\n" + prompt
-        # Placeholder for real IBM SDK call
         return (
             "[IBM Granite/Watson simulated response]\n"
             "(Replace this with real SDK call)\n\n"
             + prompt
         )
 
-CATEGORIES = { ... }  # leave as in original
-
+# -------------------------
+# Helpers
+# -------------------------
 def categorize(desc: str) -> str:
     desc_l = (desc or "").lower()
     for cat, keys in CATEGORIES.items():
@@ -82,6 +112,7 @@ def categorize(desc: str) -> str:
             return cat
     return "other"
 
+
 @dataclass
 class UserProfile:
     name: str
@@ -91,32 +122,42 @@ class UserProfile:
     monthly_income: float
     risk_tolerance: str
     goals: str
+
     def style_prompt(self) -> str:
         if self.user_type.lower().startswith("stud"):
             return (
                 "Explain like a friendly mentor to a student. Keep it clear and concise, "
-                "use practical examples and low‑jargon."
+                "use practical examples and low-jargon."
             )
         return (
             "Explain like a professional financial coach. Be precise, structured, and include "
-            "brief rationale with trade‑offs."
+            "brief rationale with trade-offs."
         )
 
+
 def load_transactions(uploaded_file: Optional[io.BytesIO]) -> pd.DataFrame:
     if uploaded_file is None:
-        # Demo data
-        data = { ... }  # as per original
+        data = {
+            "date": ["2025-08-01", "2025-08-02", "2025-08-03"],
+            "description": ["grocery store", "uber ride", "netflix subscription"],
+            "amount": [-1500, -300, -5000]
+        }
         df = pd.DataFrame(data)
     else:
         try:
             df = pd.read_csv(uploaded_file)
         except Exception as e:
             st.error(f"Could not read CSV: {e}. Showing demo data.")
-            data = { ... }
+            data = {
+                "date": ["2025-08-01", "2025-08-02", "2025-08-03"],
+                "description": ["grocery store", "uber ride", "netflix subscription"],
+                "amount": [-1500, -300, -5000]
+            }
             df = pd.DataFrame(data)
     df["category"] = df["description"].apply(categorize)
     return df
 
+
 def budget_summary(df: pd.DataFrame, monthly_income_hint: Optional[float] = None) -> Dict[str, float]:
     df["month"] = pd.to_datetime(df["date"]).dt.to_period("M").astype(str)
     income = df.loc[df["amount"] > 0, "amount"].sum()
@@ -136,12 +177,20 @@ def budget_summary(df: pd.DataFrame, monthly_income_hint: Optional[float] = None
         "top_spend_json": top_spend.to_json(),
     }
 
+
 def spending_suggestions(df: pd.DataFrame, profile: UserProfile) -> List[str]:
     tips = []
     summary = budget_summary(df, monthly_income_hint=profile.monthly_income)
-    # ... rest unchanged
+    if summary["savings_rate_pct"] < 10:
+        tips.append("Your savings rate is low. Try setting aside at least 20% of your income.")
+    if "entertainment" in df["category"].values:
+        tips.append("Entertainment spending is high. Consider limiting subscriptions or outings.")
+    if "food" in df["category"].values:
+        tips.append("Track food expenses. Cooking at home can save money.")
+    if not tips:
+        tips.append("Good job! Your spending looks balanced.")
+    return tips
 
-INTENT_PATTERNS = { ... }
 
 def detect_intent(text: str) -> str:
     t = text.lower()
@@ -150,22 +199,40 @@ def detect_intent(text: str) -> str:
             return k
     return "general"
 
+
 def build_system_prompt(profile: UserProfile) -> str:
-    # ... unchanged
+    return (
+        f"You are a financial assistant. User profile: {profile}. "
+        f"Respond in style: {profile.style_prompt()}"
+    )
+
 
 def craft_user_prompt(query: str, intent: str, summary: Dict[str, float]) -> str:
-    pass
-    # ... unchanged
+    return f"User asked about {intent}: {query}\nBudget summary: {summary}"
 
+# -------------------------
+# Sidebar Inputs
+# -------------------------
 with st.sidebar:
-    # Inputs (unchanged)
-    # ...
-
-profile = UserProfile(
-    # ... unchanged
-)
+    provider_choice = st.radio(
+        "Select AI Provider",
+        ["HuggingFace", "IBM Granite Watson", "Auto (Best Available)"]
+    )
+    uploaded = st.file_uploader("Upload your transactions CSV", type=["csv"])
+    st.markdown("### Profile")
+    profile = UserProfile(
+        name=st.text_input("Name", "Rahul"),
+        user_type=st.selectbox("User Type", ["Student", "Professional"]),
+        age=st.number_input("Age", 18, 100, 25),
+        country=st.text_input("Country", "India"),
+        monthly_income=st.number_input("Monthly Income", 0.0, 1e7, 50000.0),
+        risk_tolerance=st.selectbox("Risk Tolerance", ["Low", "Medium", "High"]),
+        goals=st.text_area("Financial Goals", "Save for emergency fund and invest in mutual funds")
+    )
 
-# Provider initialization block
+# -------------------------
+# Provider Initialization
+# -------------------------
 if not st.session_state["provider_inited"]:
     st.session_state["providers"]["ibm"] = IBMGraniteWatsonProvider(
         watson_api_key=os.getenv("IBM_WATSON_API_KEY"),
@@ -173,7 +240,7 @@ if not st.session_state["provider_inited"]:
         granite_key=os.getenv("IBM_GRANITE_API_KEY"),
     )
     st.session_state["providers"]["hf"] = HuggingFaceProvider()
-    # Choose provider based on user choice and credentials
+
     chosen = "huggingface"
     ibm_ok = st.session_state["providers"]["ibm"].ok
     if provider_choice.startswith("IBM") and ibm_ok:
@@ -183,7 +250,6 @@ if not st.session_state["provider_inited"]:
     st.session_state["provider_name"] = chosen
     st.session_state["provider_inited"] = True
 
-# Always pick the right provider, even after sidebar changes
 provider_name = st.session_state["provider_name"]
 if provider_choice.startswith("IBM"):
     provider_name = "ibm_granite_watson" if st.session_state["providers"]["ibm"].ok else "huggingface"
@@ -192,12 +258,17 @@ elif provider_choice.startswith("Auto"):
 else:
     provider_name = "huggingface"
 st.session_state["provider_name"] = provider_name
+
 provider = (
     st.session_state["providers"]["ibm"] if provider_name == "ibm_granite_watson"
     else st.session_state["providers"]["hf"]
 )
 
+# -------------------------
+# Layout
+# -------------------------
 col_chat, col_right = st.columns([0.62, 0.38])
+
 with col_right:
     st.subheader("📊 Budget Summary")
     df = load_transactions(uploaded)
@@ -208,6 +279,7 @@ with col_right:
     m2.metric("Expenses (₹)", f"{summary['expense_total']:.0f}")
     m3.metric("Net (₹)", f"{summary['net_savings']:.0f}")
     m4.metric("Savings Rate", f"{summary['savings_rate_pct']:.1f}%")
+
     st.markdown("### 🧠 AI Spending Suggestions")
     for tip in spending_suggestions(df, profile):
         st.write("• ", tip)
@@ -238,4 +310,4 @@ st.markdown("""
 **Disclaimers**  
 This chatbot provides educational information only and is **not** financial, tax, or legal advice.  
 Tax rules change frequently; consult a qualified professional for personalized advice.
-""")
+""")