Update app.py

app.py

@@ -16,9 +16,9 @@ logger.info(f"Using device: {device}")
 
 # Response cache
 response_cache = {
-    "hi": "Hello! I'm your financial advisor. How can I help with investing?",
-    "hello": "Hello! I'm your financial advisor. How can I help with investing?",
-    "hey": "Hi there! Ready to discuss investment goals?",
+    "hi": "Hello! I'm FinChat, your financial advisor. How can I help with investing?",
+    "hello": "Hello! I'm FinChat, your financial advisor. How can I help with investing?",
+    "hey": "Hi there! Ready to discuss investment goals with FinChat?",
     "what is better individual stocks or etfs?": (
         "Here’s a comparison of individual stocks vs. ETFs:\n"
         "1. **Individual Stocks**: Offer high potential returns by investing in specific companies (e.g., Apple) but carry higher risk due to lack of diversification. Require active research.\n"
@@ -26,6 +26,24 @@ response_cache = {
         "3. **Recommendation**: Beginners should start with ETFs for stability; experienced investors may add stocks for growth.\n"
         "Consult a financial planner for personalized advice."
     ),
+    "is $100 per month enough to invest?": (
+        "Yes, $100 per month is enough to start investing. Here’s why and how:\n"
+        "1. **Feasibility**: Many brokerages (e.g., Fidelity, Vanguard) have no minimums, and commission-free trading eliminates fee concerns.\n"
+        "2. **Options**: You can buy fractional shares of ETFs (e.g., VOO, ~$500/share) or low-cost stocks, making $100 viable.\n"
+        "3. **Strategy**: Use dollar-cost averaging to invest $100 monthly, reducing market timing risks.\n"
+        "4. **Growth**: Over time, $100 monthly can grow significantly with compound interest (e.g., 7% annual return could yield ~$40,000 in 20 years).\n"
+        "5. **Considerations**: Ensure you have an emergency fund first; diversify to manage risk.\n"
+        "Consult a financial planner for tailored advice."
+    ),
+    "can i invest $100 a month?": (
+        "Yes, $100 a month is sufficient to start investing. Here’s how:\n"
+        "1. **Brokerage**: Open an account with Fidelity or Vanguard, which offer no minimums.\n"
+        "2. **Investments**: Buy fractional shares of ETFs like VOO ($100 buys ~0.2 shares) or low-cost index funds.\n"
+        "3. **Approach**: Use dollar-cost averaging to invest $100 monthly for steady growth.\n"
+        "4. **Long-Term**: At a 7% annual return, $100 monthly could grow to ~$40,000 in 20 years.\n"
+        "5. **Tips**: Prioritize an emergency fund and diversify.\n"
+        "Consult a financial planner."
+    ),
     "hi, give me step-by-step investing advice": (
         "Here’s a step-by-step guide to start investing:\n"
         "1. Open a brokerage account (e.g., Fidelity, Vanguard) if 18 or older.\n"
@@ -142,9 +160,13 @@ except Exception as e:
 
 # Pre-tokenize prompt prefix
 prompt_prefix = (
-    "You are a financial advisor. Provide numbered list advice for investing prompts. "
-    "Avoid repetition.\n\n"
-    "Example: Q: Give investing tips\nA: 1. Open a brokerage.\n2. Buy ETFs like VOO.\n3. Use dollar-cost averaging.\n\n"
+    "You are a financial advisor. Provide detailed, numbered list advice with clear reasoning for investing prompts. "
+    "Avoid repetition and incomplete answers. Explain why each step or choice is beneficial.\n\n"
+    "Example: Q: Can I invest $100 a month?\n"
+    "A: Yes, $100 a month is sufficient to start. Here’s how:\n"
+    "1. Open a brokerage account (e.g., Fidelity): No minimums allow small investments, making it accessible.\n"
+    "2. Buy fractional ETF shares (e.g., VOO): Diversifies risk across many companies.\n"
+    "3. Use dollar-cost averaging: Investing regularly reduces market timing risks.\n\n"
     "Q: "
 )
 prefix_tokens = tokenizer(prompt_prefix, return_tensors="pt", truncation=True, max_length=512).to(device)
@@ -174,7 +196,7 @@ def chat_with_model(user_input, history=None):
         # Skip model for short prompts
         if len(user_input.strip()) <= 5:
             logger.info("Short prompt, returning default response")
-            response = "Hello! I'm your financial advisor. Ask about investing!"
+            response = "Hello! I'm FinChat, your financial advisor. Ask about investing!"
             logger.info(f"Chatbot response: {response}")
             history = history or []
             history.append({"role": "user", "content": user_input})
@@ -196,12 +218,13 @@ def chat_with_model(user_input, history=None):
         
         # Generate response
         with torch.inference_mode():
+            logger.info("Generating response with model")
             outputs = model.generate(
                 **inputs,
-                max_new_tokens=30,
-                min_length=10,
+                max_new_tokens=80,
+                min_length=20,
                 do_sample=True,
-                temperature=0.7,
+                temperature=0.6,
                 top_p=0.9,
                 repetition_penalty=1.2,
                 pad_token_id=tokenizer.eos_token_id
@@ -210,14 +233,9 @@ def chat_with_model(user_input, history=None):
         response = response[len(full_prompt):].strip() if response.startswith(full_prompt) else response
         logger.info(f"Chatbot response: {response}")
         
-        # Update cache and save to file
+        # Update cache
         response_cache[cache_key] = response
-        try:
-            with open(cache_file, 'w') as f:
-                json.dump(response_cache, f, indent=2)
-            logger.info("Updated cache.json")
-        except Exception as e:
-            logger.warning(f"Failed to update cache.json: {e}")
+        logger.info("Cache miss, added to in-memory cache")
         
         # Update history
         history = history or []
@@ -234,10 +252,26 @@ def chat_with_model(user_input, history=None):
         history.append({"role": "assistant", "content": response})
         return response, history
 
+# Save cache on exit
+def save_cache():
+    try:
+        with open(cache_file, 'w') as f:
+            json.dump(response_cache, f, indent=2)
+        logger.info("Saved cache to cache.json")
+    except Exception as e:
+        logger.warning(f"Failed to save cache.json: {e}")
+
 # Create Gradio interface
 logger.info("Initializing Gradio interface")
 try:
-    with gr.Blocks() as interface:
+    with gr.Blocks(title="FinChat: An LLM based on distilgpt2 model") as interface:
+        gr.Markdown(
+            """
+            # FinChat: An LLM based on distilgpt2 model
+            FinChat provides financial advice using the lightweight distilgpt2 model, optimized for fast, detailed responses.
+            Ask about investing strategies, ETFs, stocks, or budgeting to get started!
+            """
+        )
         chatbot = gr.Chatbot(type="messages")
         msg = gr.Textbox(label="Your message")
         submit = gr.Button("Send")
@@ -253,6 +287,7 @@ try:
             outputs=[msg, chatbot]
         )
         clear.click(lambda: None, None, chatbot)
+    logger.info("Gradio interface initialized successfully")
 except Exception as e:
     logger.error(f"Error initializing Gradio interface: {e}")
     raise
@@ -265,5 +300,9 @@ if __name__ == "__main__" and not os.getenv("HF_SPACE"):
     except Exception as e:
         logger.error(f"Error launching interface: {e}")
         raise
+    finally:
+        save_cache()
 else:
-    logger.info("Running in Hugging Face Spaces, interface defined but not launched")
+    logger.info("Running in Hugging Face Spaces, interface defined but not launched")
+    import atexit
+    atexit.register(save_cache)