Update app.py

app.py

@@ -129,14 +129,26 @@ You have access to:
 3. A powerful index calculator for custom analysis
 When users ask about stocks or indices, fetch live data. Provide clear explanations with numbers."""
 
+# Mapping for common company names to tickers (for global stocks)
+GLOBAL_TICKER_MAP = {
+    'APPLE': 'AAPL',
+    'GOOGLE': 'GOOGL',
+    'MICROSOFT': 'MSFT',
+    'AMAZON': 'AMZN',
+    'TESLA': 'TSLA',
+    'META': 'META',
+    'FACEBOOK': 'META',
+    'NETFLIX': 'NFLX',
+}
+
 # ========== Core conversational function ==========
-def process_query(query: str, history: List[List], exchange: str) -> Tuple[str, List[List], pd.DataFrame, str]:
+def process_query(query: str, history: List[Dict], exchange: str) -> Tuple[str, List[Dict], pd.DataFrame, str]:
     """
     Process user query with real-time data awareness.
-    history is a list of lists: [[user_msg1, assistant_msg1], [user_msg2, assistant_msg2], ...]
+    history is a list of dicts: [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}, ...]
     Returns:
         - response_text: str (the assistant's reply)
-        - updated_history: list of lists (new chat history)
+        - updated_history: list of dicts (new chat history)
         - data_preview: pd.DataFrame
         - status: str (unused)
     """
@@ -148,12 +160,13 @@ def process_query(query: str, history: List[List], exchange: str) -> Tuple[str,
     indian_stocks = ['RELIANCE', 'TCS', 'INFY', 'HDFCBANK', 'ICICIBANK', 'SBIN', 
                      'ITC', 'BHARTIARTL', 'KOTAKBANK', 'LT', 'WIPRO', 'AXISBANK']
     
+    query_upper = query.upper()
     query_lower = query.lower()
     
     # Real-time data handling
-    if any(stock.lower() in query_lower for stock in indian_stocks) or 'nifty' in query_lower or 'sensex' in query_lower:
+    if any(stock in query_upper for stock in indian_stocks) or 'NIFTY' in query_upper or 'SENSEX' in query_upper:
         for stock in indian_stocks:
-            if stock.lower() in query_lower:
+            if stock in query_upper:
                 try:
                     # Build symbol with correct suffix for Indian exchange
                     if exchange == "NSE":
@@ -178,7 +191,7 @@ def process_query(query: str, history: List[List], exchange: str) -> Tuple[str,
                     response_text = f"Error fetching data: {str(e)}"
                 break
         
-        if 'nifty' in query_lower and not response_text:
+        if 'NIFTY' in query_upper and not response_text:
             try:
                 nifty_data = global_api.get_stock_info('^NSEI')
                 if nifty_data:
@@ -187,22 +200,36 @@ def process_query(query: str, history: List[List], exchange: str) -> Tuple[str,
             except Exception as e:
                 response_text = f"Error fetching Nifty: {str(e)}"
     
-    elif any(ticker in query.upper() for ticker in ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA']):
-        tickers = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA']
-        for ticker in tickers:
-            if ticker in query.upper():
-                try:
-                    data = global_api.get_stock_info(ticker)
-                    if data:
-                        response_text = f"**{data['company_name']} ({ticker})**\n\n"
-                        response_text += f"💰 Current Price: ${data['last_price']:,.2f}\n"
-                        response_text += f"📊 Change: ${data['change']:+.2f} ({data['percent_change']:+.2f}%)\n"
-                        response_text += f"📈 Day Range: ${data['day_low']} - ${data['day_high']}\n"
-                        response_text += f"💹 Market Cap: ${data['market_cap']/1e9:,.2f}B\n"
-                        data_preview = pd.DataFrame([data])
-                except Exception as e:
-                    response_text = f"Error fetching data: {str(e)}"
+    # Check for global stock tickers or company names
+    else:
+        # Try to find a ticker in the query
+        ticker = None
+        # First check if any known ticker appears directly
+        known_tickers = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA', 'META', 'NFLX']
+        for t in known_tickers:
+            if t in query_upper:
+                ticker = t
                 break
+        
+        # If not found, check for company names
+        if not ticker:
+            for company, t in GLOBAL_TICKER_MAP.items():
+                if company in query_upper:
+                    ticker = t
+                    break
+        
+        if ticker:
+            try:
+                data = global_api.get_stock_info(ticker)
+                if data:
+                    response_text = f"**{data['company_name']} ({ticker})**\n\n"
+                    response_text += f"💰 Current Price: ${data['last_price']:,.2f}\n"
+                    response_text += f"📊 Change: ${data['change']:+.2f} ({data['percent_change']:+.2f}%)\n"
+                    response_text += f"📈 Day Range: ${data['day_low']} - ${data['day_high']}\n"
+                    response_text += f"💹 Market Cap: ${data['market_cap']/1e9:,.2f}B\n"
+                    data_preview = pd.DataFrame([data])
+            except Exception as e:
+                response_text = f"Error fetching data: {str(e)}"
     
     # If no real-time match, use LLM
     if not response_text:
@@ -210,11 +237,10 @@ def process_query(query: str, history: List[List], exchange: str) -> Tuple[str,
             llm = get_llm()
             from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
             
-            # Convert Gradio history (list of lists) to flat list for LangChain
+            # Convert Gradio history (list of dicts) to flat list for LangChain
             flat_history = []
-            for user_msg, assistant_msg in history:
-                flat_history.append(user_msg)
-                flat_history.append(assistant_msg)
+            for turn in history:
+                flat_history.append(turn["content"])
             
             messages = [SystemMessage(content=SYSTEM_PROMPT)]
             for i, msg in enumerate(flat_history):
@@ -229,11 +255,14 @@ def process_query(query: str, history: List[List], exchange: str) -> Tuple[str,
         except Exception as e:
             response_text = f"LLM error: {str(e)}"
     
-    # Update history in Gradio's classic format (list of lists)
-    updated_history = history + [[query, response_text]]
+    # Update history in Gradio's dictionary format (list of dicts)
+    updated_history = history + [
+        {"role": "user", "content": query},
+        {"role": "assistant", "content": response_text}
+    ]
     # Keep only last 10 exchanges (20 messages)
-    if len(updated_history) > 10:
-        updated_history = updated_history[-10:]
+    if len(updated_history) > 20:
+        updated_history = updated_history[-20:]
     
     return response_text, updated_history, data_preview, ""
 
@@ -257,17 +286,20 @@ def fetch_live_stock(symbol: str, exchange: str):
             fig.update_layout(height=300)
             return formatted, formatted['last_price'], formatted['change'], formatted['percent_change'], fig
     else:
-        data = global_api.get_stock_info(symbol)
-        if data:
-            fig = go.Figure()
-            fig.add_trace(go.Indicator(
-                mode="number+delta",
-                value=data['last_price'],
-                number={'prefix': '$'},
-                delta={'reference': data['last_price'] - data['change']}
-            ))
-            fig.update_layout(height=300)
-            return data, data['last_price'], data['change'], data['percent_change'], fig
+        # For global, try to map common names
+        ticker = symbol.upper()
+        if ticker in GLOBAL_TICKER_MAP.values() or ticker in ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA', 'META', 'NFLX']:
+            data = global_api.get_stock_info(ticker)
+            if data:
+                fig = go.Figure()
+                fig.add_trace(go.Indicator(
+                    mode="number+delta",
+                    value=data['last_price'],
+                    number={'prefix': '$'},
+                    delta={'reference': data['last_price'] - data['change']}
+                ))
+                fig.update_layout(height=300)
+                return data, data['last_price'], data['change'], data['percent_change'], fig
     return {}, 0, 0, 0, go.Figure()
 
 def build_custom_index(symbols_df: pd.DataFrame, index_type: str, exchange: str):
@@ -288,6 +320,7 @@ def build_custom_index(symbols_df: pd.DataFrame, index_type: str, exchange: str)
             else:
                 price = 0
         else:
+            # For global, try direct ticker
             data = global_api.get_stock_info(symbol)
             price = data.get('last_price', 0) if data else 0
         weights.append(weight)