Update app.py

app.py

@@ -119,10 +119,11 @@ def parse_dates(query):
         end_date = datetime.now()
         start_date = end_date - period
         return start_date, end_date
-    # Default to 1 year
-    end_date = datetime.now()
-    start_date = end_date - timedelta(days=365)
-    return start_date, end_date
+    else:
+        # Default to 1 year
+        end_date = datetime.now()
+        start_date = end_date - timedelta(days=365)
+        return start_date, end_date
 
 def find_closest_symbol(input_symbol):
     input_symbol = input_symbol.upper()
@@ -159,11 +160,8 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 
 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
@@ -174,59 +172,34 @@ def generate_response(user_query, enable_thinking=False):
         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:
-                summary = "Invalid date range provided. Please specify a valid range, e.g., 'between 2010 and 2020'."
+                return "Invalid date range."
             else:
                 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 ${avg_price:.2f}."
+                        return f"${avg_price:.2f}"
                     else:
-                        summary = f"No data available for {symbol} in the specified period."
+                        return "No data."
                 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 (geometric average annual return) for {symbol} over the period from {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')} is {growth_rate:.2f}%."
+                        return f"{growth_rate:.2f}%"
                     else:
-                        summary = f"No data available for {symbol} in the specified period."
+                        return "No data."
                 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: ${projected:.2f}."
+                    return f"${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')."
-    
-    # Enhanced system prompt for better guidance
-    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 the provided data summary. If no data is available, suggest alternatives politely. "
-        "Use the data summary to inform your response where relevant."
-    )
-    
-    # Integrate thinking if enabled (simple chain-of-thought prompt)
-    thinking_prompt = "Think step by step before responding. " if enable_thinking else ""
-    
-    # Prepare messages for the model
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": f"{thinking_prompt}Query: {user_query}\nData summary: {summary if summary else 'No specific data computed; respond generally based on knowledge.'}"}
-    ]
-    
-    # Generate response using the model
-    input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
-    outputs = model.generate(input_ids, max_new_tokens=200, do_sample=True, temperature=0.7)
-    response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
-    
-    return response
+            return "Invalid symbol."
+    else:
+        return "Hello! Ask about stocks or investments."
 
 # Gradio interface setup
 demo = gr.Interface(