Update app.py

app.py

@@ -1,12 +1,12 @@
 import os
-import sys
 import gradio as gr
 import pandas as pd
 import numpy as np
 import plotly.graph_objects as go
+import yfinance as yf
 from datetime import datetime
+from typing import List, Tuple, Dict, Optional
 import json
-from typing import Dict, List, Tuple, Optional
 
 # ========== Optional memory monitoring ==========
 try:
@@ -16,7 +16,7 @@ try:
 except ImportError:
     pass
 
-# ========== Lazy imports for heavy modules ==========
+# ========== Lazy LLM import ==========
 def get_llm():
     from langchain_openai import ChatOpenAI
     from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
@@ -30,21 +30,92 @@ def get_llm():
         temperature=0.3,
     )
 
-def get_realtime_calculator():
-    from data_sources import RealTimeIndexCalculator, IndianStockAPI, GlobalStockAPI
-    return RealTimeIndexCalculator(), IndianStockAPI(), GlobalStockAPI()
+# ========== Stock API classes using yfinance ==========
+class IndianStockAPI:
+    """Fetch Indian stock data via yfinance (NSE symbols with .NS)"""
+    
+    def get_stock_price(self, symbol: str, exchange: str = "NSE"):
+        """Get stock data for Indian market"""
+        try:
+            # For NSE/BSE, yfinance uses .NS suffix
+            yf_symbol = symbol + ".NS" if exchange == "NSE" else symbol + ".BO"
+            ticker = yf.Ticker(yf_symbol)
+            info = ticker.info
+            hist = ticker.history(period="1d")
+            if hist.empty:
+                return None
+            
+            last_price = hist['Close'].iloc[-1]
+            prev_close = info.get('regularMarketPreviousClose', last_price)
+            change = last_price - prev_close
+            pct_change = (change / prev_close) * 100 if prev_close else 0
+            
+            return {
+                'data': [{
+                    'symbol': symbol,
+                    'last_price': last_price,
+                    'change': change,
+                    'percent_change': pct_change,
+                    'day_low': hist['Low'].iloc[-1],
+                    'day_high': hist['High'].iloc[-1],
+                    'year_low': info.get('fiftyTwoWeekLow', 0),
+                    'year_high': info.get('fiftyTwoWeekHigh', 0),
+                    'market_cap': info.get('marketCap', 0) / 1e7,  # in Cr
+                    'pe_ratio': info.get('trailingPE', 0),
+                    'company_name': info.get('longName', symbol)
+                }]
+            }
+        except Exception as e:
+            print(f"Error fetching Indian stock {symbol}: {e}")
+            return None
+    
+    def format_for_index(self, data):
+        """Format API response to match expected structure"""
+        if data and 'data' in data and len(data['data']) > 0:
+            return data['data'][0]
+        return {}
 
-# ========== Simple memory (optional, not heavily used) ==========
-class SimpleMemory:
+class GlobalStockAPI:
+    """Fetch global stock data via yfinance"""
+    
+    def get_stock_info(self, symbol: str):
+        """Get stock data for global markets"""
+        try:
+            ticker = yf.Ticker(symbol)
+            info = ticker.info
+            hist = ticker.history(period="1d")
+            if hist.empty:
+                return None
+            
+            last_price = hist['Close'].iloc[-1]
+            prev_close = info.get('regularMarketPreviousClose', last_price)
+            change = last_price - prev_close
+            pct_change = (change / prev_close) * 100 if prev_close else 0
+            
+            return {
+                'symbol': symbol,
+                'company_name': info.get('longName', symbol),
+                'last_price': last_price,
+                'change': change,
+                'percent_change': pct_change,
+                'day_low': hist['Low'].iloc[-1],
+                'day_high': hist['High'].iloc[-1],
+                'market_cap': info.get('marketCap', 0),
+            }
+        except Exception as e:
+            print(f"Error fetching global stock {symbol}: {e}")
+            return None
+
+class RealTimeIndexCalculator:
+    """Placeholder for index calculations (not heavily used)"""
     def __init__(self):
-        self.messages = []
-    def add_message(self, message: str, role: str):
-        self.messages.append({'role': role, 'content': message})
-    def get_history(self):
-        return self.messages
+        pass
 
-memory = SimpleMemory()
+# ========== Helper to get API instances ==========
+def get_realtime_calculator():
+    return RealTimeIndexCalculator(), IndianStockAPI(), GlobalStockAPI()
 
+# ========== System prompt ==========
 SYSTEM_PROMPT = """You are an expert financial analyst specializing in index numbers and real-time market data. 
 You help users understand, calculate, and analyze various types of indices including:
 - Laspeyres, Paasche, and Fisher price indices
@@ -59,11 +130,7 @@ When users ask about stocks or indices, fetch live data. Provide clear explanati
 
 # ========== Core conversational function ==========
 def process_query(query: str, history: List, exchange: str) -> Tuple[str, List, pd.DataFrame, str]:
-    """
-    Process user query with real-time data awareness
-    """
     rt_calc, indian_api, global_api = get_realtime_calculator()
-    
     response_text = ""
     data_preview = pd.DataFrame()
     
@@ -127,7 +194,6 @@ def process_query(query: str, history: List, exchange: str) -> Tuple[str, List,
             
             messages = [SystemMessage(content=SYSTEM_PROMPT)]
             if history:
-                # history is a flat list: [user1, assistant1, user2, assistant2, ...]
                 for i in range(0, len(history), 2):
                     if i < len(history):
                         messages.append(HumanMessage(content=history[i]))
@@ -140,7 +206,7 @@ def process_query(query: str, history: List, exchange: str) -> Tuple[str, List,
         except Exception as e:
             response_text = f"LLM error: {str(e)}"
     
-    # Update history (keep last 10 exchanges to avoid memory bloat)
+    # Update history (keep last 10 exchanges)
     updated_history = history + [query, response_text]
     if len(updated_history) > 20:
         updated_history = updated_history[-20:]
@@ -155,7 +221,6 @@ def fetch_live_stock(symbol: str, exchange: str):
         data = indian_api.get_stock_price(symbol, exchange)
         if data and 'data' in data:
             formatted = indian_api.format_for_index(data)
-            # Simple chart
             fig = go.Figure()
             fig.add_trace(go.Indicator(
                 mode="number+delta",
@@ -191,8 +256,7 @@ def build_custom_index(symbols_df: pd.DataFrame, index_type: str, exchange: str)
         if exchange in ["NSE", "BSE"]:
             data = indian_api.get_stock_price(symbol, exchange)
             if data and 'data' in data:
-                # Adjust based on actual API structure
-                price = data['data'][0]['last_price'] if 'data' in data and data['data'] else 0
+                price = data['data'][0]['last_price']
             else:
                 price = 0
         else:
@@ -203,15 +267,18 @@ def build_custom_index(symbols_df: pd.DataFrame, index_type: str, exchange: str)
         total_weight += weight
     
     # Normalize weights if total != 1
-    if total_weight != 1:
+    if total_weight != 1 and total_weight > 0:
         weights = [w/total_weight for w in weights]
     
-    if index_type == "Price-Weighted":
-        index_value = sum(prices) / len(prices) if prices else 0
-    elif index_type == "Equal-Weighted":
-        index_value = sum(prices) / len(prices) if prices else 0
-    elif index_type == "Market Cap-Weighted":
-        index_value = sum(w * p for w, p in zip(weights, prices)) if prices else 0
+    if prices:
+        if index_type == "Price-Weighted":
+            index_value = sum(prices) / len(prices)
+        elif index_type == "Equal-Weighted":
+            index_value = sum(prices) / len(prices)
+        elif index_type == "Market Cap-Weighted":
+            index_value = sum(w * p for w, p in zip(weights, prices))
+        else:
+            index_value = 0
     else:
         index_value = 0
     
@@ -250,13 +317,13 @@ def compare_live_indices(symbols_str: str, exchange: str):
     return data, fig
 
 # ========== Main Gradio interface ==========
-with gr.Blocks(title="AI-Powered Index Analyst with Real-Time Data", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="AI-Powered Index Analyst with Real-Time Data") as demo:
     gr.Markdown("""
     # 📈 AI-Powered Index Analyst with Real-Time Market Data
     
     Welcome to your intelligent financial index assistant! This enhanced version includes:
-    - **Real-time Indian stock data** from NSE/BSE (free, no API key) [citation:1]
-    - **Global market data** via Yahoo Finance [citation:2]
+    - **Real-time Indian stock data** from NSE/BSE via Yahoo Finance
+    - **Global market data** via Yahoo Finance
     - **Custom index builder** with live prices
     - **Conversational AI** for market insights
     - **Multiple index formulas** (Laspeyres, Paasche, Fisher, etc.)
@@ -284,7 +351,6 @@ with gr.Blocks(title="AI-Powered Index Analyst with Real-Time Data", theme=gr.th
         data_output = gr.DataFrame(label="Data Preview")
         calc_output = gr.Textbox(label="Calculation Results", lines=3)
         
-        # Output mapping: (response, updated_history, data_preview, status)
         submit.click(
             process_query,
             inputs=[msg, chat_history, exchange_selector],
@@ -309,9 +375,9 @@ with gr.Blocks(title="AI-Powered Index Analyst with Real-Time Data", theme=gr.th
                 ### 📡 Real-Time Market Data
                 
                 Fetch live stock prices from:
-                - **NSE** (National Stock Exchange of India) [citation:1]
-                - **BSE** (Bombay Stock Exchange) [citation:1]
-                - **Global Markets** via Yahoo Finance [citation:2]
+                - **NSE** (National Stock Exchange of India) via Yahoo Finance
+                - **BSE** (Bombay Stock Exchange) via Yahoo Finance
+                - **Global Markets** via Yahoo Finance
                 """)
                 
                 exchange = gr.Radio(
@@ -373,38 +439,11 @@ with gr.Blocks(title="AI-Powered Index Analyst with Real-Time Data", theme=gr.th
         )
     
     with gr.Tab("📊 Index Calculator"):
-        try:
-            from app import create_index_ui, calculate_indices
-        except ImportError:
-            # Stub functions if module not available
-            def create_index_ui():
-                with gr.Blocks():
-                    gr.Markdown("Index Calculator placeholder – implement if needed.")
-                return {}
-            def calculate_indices(file, items, p0, q0, p1, q1):
-                return "Calculation placeholder", 0, 0, 0, go.Figure()
-        
-        calc_ui = create_index_ui()
-        if calc_ui and 'btn' in calc_ui:
-            # Connect the calculator button if UI defined
-            calc_ui["btn"].click(
-                calculate_indices,
-                inputs=[
-                    calc_ui.get("file"),
-                    calc_ui.get("items"),
-                    calc_ui.get("p0"),
-                    calc_ui.get("q0"),
-                    calc_ui.get("p1"),
-                    calc_ui.get("q1")
-                ],
-                outputs=[
-                    calc_ui.get("results"),
-                    calc_ui.get("laspeyres"),
-                    calc_ui.get("paasche"),
-                    calc_ui.get("fisher"),
-                    calc_ui.get("plot")
-                ]
-            )
+        # Simplified placeholder – you can expand with actual calculations later
+        gr.Markdown("""
+        ### Index Calculator
+        This tab is under construction. For now, use the Conversational Assistant to ask about index calculations.
+        """)
     
     with gr.Tab("🔄 Index Comparison"):
         comp_ui = create_comparison_ui()
@@ -417,11 +456,10 @@ with gr.Blocks(title="AI-Powered Index Analyst with Real-Time Data", theme=gr.th
     gr.Markdown("""
     ---
     ### 📚 Resources & Credits
-    - [Free Indian Stock Market API](https://github.com/0xramm/Indian-Stock-Market-API) - No API key required [citation:1]
-    - [yfinance](https://pypi.org/project/yfinance/) - Yahoo Finance Python library [citation:2]
+    - Data provided by [Yahoo Finance](https://finance.yahoo.com/) via [yfinance](https://pypi.org/project/yfinance/)
     - [CFA Institute](https://www.cfainstitute.org)
     
-    *Note: Free APIs may have rate limits. The Indian API caches data for 60 seconds.*
+    *Note: Free APIs may have rate limits. Data is cached for performance.*
     """)
 
 if __name__ == "__main__":