Update app.py

app.py

@@ -52,10 +52,10 @@ except Exception as e:
     print(f"Error prefetching data: {e}")
     all_data = {ticker: pd.DataFrame() for ticker in tickers}  # Initialize empty DataFrames on failure
 
-# Create a DataFrame with 'Adj Close' columns for each ticker
+# Create a DataFrame with 'Close' columns for each ticker
 series_list = []
 for ticker, data in all_data.items():
-    if not data.empty:
+    if not data.empty and 'Close' in data.columns:
         s = data['Close']
         s.name = ticker
         series_list.append(s)
@@ -69,19 +69,25 @@ available_symbols = ['TSLA', 'MSFT', 'NVDA', 'GOOG', 'AMZN', 'SPY', 'AAPL', 'MET
 
 @lru_cache(maxsize=100)
 def fetch_stock_data(symbol, start_date, end_date):
-    if symbol in all_data and not all_data[symbol].empty:
-        # Use preloaded data and slice by date
-        hist = all_data[symbol]
-        return hist[(hist.index >= start_date) & (hist.index <= end_date)]
-    else:
-        # Fetch on-demand with yfinance
-        try:
+    try:
+        # Validate dates
+        start = datetime.strptime(start_date, '%Y-%m-%d')
+        end = datetime.strptime(end_date, '%Y-%m-%d')
+        if start >= end:
+            print(f"Invalid date range: {start_date} to {end_date}")
+            return None
+        if symbol in all_data and not all_data[symbol].empty:
+            # Use preloaded data and slice by date
+            hist = all_data[symbol]
+            return hist[(hist.index >= start_date) & (hist.index <= end_date)]
+        else:
+            # Fetch on-demand with yfinance
             ticker = yf.Ticker(symbol)
             hist = ticker.history(start=start_date, end=end_date, auto_adjust=True)
             return hist
-        except Exception as e:
-            print(f"Error fetching data for {symbol}: {e}")
-            return None
+    except Exception as e:
+        print(f"Error fetching data for {symbol}: {e}")
+        return None
 
 def parse_dates(query):
     # Handle year ranges like "between 2010 and 2020"
@@ -133,4 +139,86 @@ def find_closest_symbol(input_symbol):
 
 def calculate_growth_rate(start_date, end_date, symbol):
     hist = fetch_stock_data(symbol, start_date.strftime('%Y-%m-%d'), end_date.strftime('%Y-%m-%d'))
-    if hist is
+    if hist is None or hist.empty or 'Close' not in hist.columns:
+        print(f"No valid data for {symbol} from {start_date} to {end_date}")
+        return None
+    beginning_value = hist.iloc[0]['Close']
+    ending_value = hist.iloc[-1]['Close']
+    years = (end_date - start_date).days / 365.25
+    if years <= 0:
+        return 0
+    total_return = ending_value / beginning_value
+    cagr = total_return ** (1 / years) - 1
+    return cagr * 100
+
+def calculate_investment(principal, years, annual_return=0.07):
+    return principal * (1 + annual_return) ** years
+
+model_name = "HuggingFaceTB/SmolLM-135M-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+)
+
+def generate_response(user_query, enable_thinking=False):
+    print(f"Processing query: {user_query}")  # Debugging
+    stock_keywords = ['stock', 'growth', 'investment', 'price', 'return', 'cagr']
+    is_stock_query = any(keyword in user_query.lower() for keyword in stock_keywords)
+    print(f"Is stock query: {is_stock_query}")  # Debugging
+    summary = ""
+    if is_stock_query:
+        # Try to find symbol from company name or ticker
+        symbol = None
+        for company, ticker in company_to_ticker.items():
+            if company in user_query.lower():
+                symbol = ticker
+                break
+        if not symbol:
+            symbol_match = re.search(r'\b([A-Z]{1,5})\b', user_query.upper())
+            symbol = find_closest_symbol(symbol_match.group(1)) if symbol_match else None
+        print(f"Detected symbol: {symbol}")  # Debugging
+        if symbol:
+            start_date, end_date = parse_dates(user_query)
+            print(f"Parsed dates: {start_date} to {end_date}")  # Debugging
+            if start_date is None or end_date is None:
+                return "Invalid date range provided. Please specify a valid range, e.g., 'between 2010 and 2020'."
+            hist = fetch_stock_data(symbol, start_date.strftime('%Y-%m-%d'), end_date.strftime('%Y-%m-%d'))
+            print(f"Data fetched for {symbol}: {'Valid' if hist is not None and not hist.empty else 'Empty/None'}")  # Debugging
+            if "average price" in user_query.lower():
+                if hist is not None and not hist.empty and 'Close' in hist.columns:
+                    avg_price = hist['Close'].mean()
+                    summary = f"The average adjusted closing price for {symbol} from {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')} is ${avg_price:.2f}."
+                else:
+                    summary = f"No data available for {symbol} in the specified period."
+            elif "cagr" in user_query.lower() or "return" in user_query.lower():
+                growth_rate = calculate_growth_rate(start_date, end_date, symbol)
+                if growth_rate is not None:
+                    summary = f"The CAGR for {symbol} over the period from {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')} is {growth_rate:.2f}%."
+                else:
+                    summary = f"No data available for {symbol} in the specified period."
+            investment_match = re.search(r'\$(\d+)', user_query)
+            if investment_match:
+                principal = float(investment_match.group(1))
+                years = (end_date - start_date).days / 365.25
+                projected = calculate_investment(principal, years)
+                summary += f" Projecting ${principal} at 7% return over {years:.1f} years: ${projected:.2f}."
+        else:
+            summary = "Could not identify a valid stock symbol in the query. Please specify a company or ticker (e.g., 'Tesla' or 'TSLA')."
+    system_prompt = (
+        "You are FinChat, a knowledgeable financial advisor. Always respond in a friendly, professional manner. "
+        "For greetings like 'Hi' or 'Hello', reply warmly, e.g., 'Hi! I'm FinChat, your financial advisor. What can I help you with today regarding stocks, investments, or advice?' "
+        "Provide accurate, concise advice based on data."
+    )
+    # Placeholder for generation logic (tokenize, generate, decode)
+    return summary or "Please provide a specific stock or investment query."
+
+# Gradio interface setup
+demo = gr.Interface(
+    fn=generate_response,
+    inputs=[gr.Textbox(lines=2, placeholder="Enter your query (e.g., 'Tesla average price between 2010 and 2020')"), gr.Checkbox(label="Enable Thinking")],
+    outputs="text",
+    title="FinChat",
+    description="Ask about stock performance, CAGR, or investments."
+)
+demo.launch()