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| import os | |
| import sys | |
| import subprocess | |
| import re | |
| import multiprocessing | |
| import atexit | |
| from collections.abc import Iterator | |
| from functools import lru_cache | |
| import datetime | |
| import time | |
| import gradio as gr | |
| import gradio.themes as themes | |
| from huggingface_hub import hf_hub_download, login | |
| import logging | |
| import pandas as pd | |
| import torch | |
| # Set up logging with more detail | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' | |
| ) | |
| logger = logging.getLogger(__name__) | |
| # Install llama-cpp-python with appropriate backend | |
| try: | |
| from llama_cpp import Llama | |
| except ModuleNotFoundError: | |
| if torch.cuda.is_available(): | |
| logger.info("Installing llama-cpp-python with CUDA support.") | |
| os.environ['CMAKE_ARGS'] = "-DLLAMA_CUDA=ON" | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "llama-cpp-python", "--force-reinstall", "--upgrade", "--no-cache-dir"]) | |
| else: | |
| logger.info("Installing llama-cpp-python without additional flags.") | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "llama-cpp-python", "--force-reinstall", "--upgrade", "--no-cache-dir"]) | |
| from llama_cpp import Llama | |
| # Install yfinance if not present | |
| try: | |
| import yfinance as yf | |
| except ModuleNotFoundError: | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "yfinance"]) | |
| import yfinance as yf | |
| # Additional imports for visualization | |
| try: | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| import io | |
| except ModuleNotFoundError: | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "matplotlib", "pillow"]) | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| import io | |
| # Constants | |
| MAX_MAX_NEW_TOKENS = 512 | |
| DEFAULT_MAX_NEW_TOKENS = 256 | |
| MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "1024")) | |
| YFINANCE_TIMEOUT = 10 | |
| CACHE_EXPIRY_HOURS = 1 | |
| DESCRIPTION = """# FinChat: Investing Q&A (Optimized for Speed) | |
| This application delivers an interactive chat interface powered by a highly efficient, small AI model adapted for addressing investing and finance inquiries through specialized prompt engineering. It ensures rapid, reasoned responses to user queries. | |
| <p>Running on CPU or GPU if available. Using Phi-2 model for faster inference. Includes performance optimizations with caching and improved error handling.</p>""" | |
| LICENSE = """<p/> | |
| --- | |
| This application employs the Phi-2 model, governed by Microsoft's Terms of Use. Refer to the [model card](https://huggingface.co/TheBloke/phi-2-GGUF) for details.""" | |
| DEFAULT_SYSTEM_PROMPT = """You are FinChat, a knowledgeable AI assistant specializing in investing and finance. Provide accurate, helpful, reasoned, and concise answers to investing questions. Always base responses on reliable information and advise users to consult professionals for personalized advice. | |
| Always respond exclusively in English. Use bullet points for clarity. | |
| Example: | |
| User: average return for TSLA between 2010 and 2020 | |
| Assistant: | |
| - TSLA CAGR (2010-2020): ~63.01% | |
| - Represents average annual return with compounding | |
| - Past performance not indicative of future results | |
| - Consult a financial advisor""" | |
| # Company name to ticker mapping | |
| COMPANY_TO_TICKER = { | |
| "opendoor": "OPEN", | |
| "tesla": "TSLA", | |
| "apple": "AAPL", | |
| "amazon": "AMZN", | |
| "microsoft": "MSFT", | |
| "google": "GOOGL", | |
| "facebook": "META", | |
| "meta": "META", | |
| "nvidia": "NVDA", | |
| "netflix": "NFLX", | |
| } | |
| # Compiled regex patterns for better performance | |
| CAGR_PATTERN = re.compile( | |
| r'(?:average\s+return|cagr)\s+(?:for\s+)?([\w\s,]+(?:and\s+[\w\s,]+)?)\s+(?:between|from)\s+(\d{4})\s+(?:and|to)\s+(\d{4})', | |
| re.IGNORECASE | |
| ) | |
| COMPOUND_INTEREST_PATTERN = re.compile( | |
| r'(?:save|invest|deposit)\s*\$?([\d,]+(?:\.\d+)?)\s*(?:right now|today)?\s*(?:under|at)\s*([\d.]+)%\s*(?:interest|rate)?\s*(?:annually|per year)?\s*(?:over|for)\s*(\d+)\s*years?', | |
| re.IGNORECASE | |
| ) | |
| # Load the model | |
| try: | |
| model_path = hf_hub_download( | |
| repo_id="TheBloke/phi-2-GGUF", | |
| filename="phi-2.Q4_K_M.gguf" | |
| ) | |
| n_gpu_layers = -1 if torch.cuda.is_available() else 0 | |
| llm = Llama( | |
| model_path=model_path, | |
| n_ctx=1024, | |
| n_batch=1024, | |
| n_threads=multiprocessing.cpu_count(), | |
| n_gpu_layers=n_gpu_layers, | |
| chat_format="chatml" | |
| ) | |
| logger.info(f"Model loaded successfully with n_gpu_layers={n_gpu_layers}.") | |
| # Warm up the model | |
| llm("Warm-up prompt", max_tokens=1, echo=False) | |
| logger.info("Model warm-up completed.") | |
| except Exception as e: | |
| logger.error(f"Error loading model: {str(e)}") | |
| raise | |
| atexit.register(llm.close) | |
| # Cache for stock data with timestamp | |
| _stock_cache = {} | |
| _cache_timestamps = {} | |
| def sanitize_ticker(ticker): | |
| """Sanitize ticker input to prevent injection and validate format""" | |
| if not ticker or not isinstance(ticker, str): | |
| return None | |
| cleaned = re.sub(r'[^A-Z0-9.-]', '', ticker.upper().strip()) | |
| if len(cleaned) > 10 or len(cleaned) == 0: | |
| return None | |
| return cleaned | |
| def validate_year_range(start_year, end_year): | |
| """Validate year inputs""" | |
| try: | |
| start = int(start_year) | |
| end = int(end_year) | |
| current_year = datetime.datetime.now().year | |
| if not (1900 <= start <= current_year): | |
| return False, f"Start year must be between 1900 and {current_year}" | |
| if not (1900 <= end <= current_year + 1): | |
| return False, f"End year must be between 1900 and {current_year + 1}" | |
| if end <= start: | |
| return False, "End year must be after start year" | |
| return True, "Valid" | |
| except ValueError: | |
| return False, "Years must be valid integers" | |
| def get_stock_data_cached(ticker, start_date, end_date, cache_key): | |
| """Cache stock data to avoid repeated API calls. cache_key includes hour for expiry.""" | |
| try: | |
| logger.info(f"Fetching data for {ticker} from {start_date} to {end_date}") | |
| data = yf.download( | |
| ticker, | |
| start=start_date, | |
| end=end_date, | |
| progress=False, | |
| auto_adjust=False, | |
| timeout=YFINANCE_TIMEOUT | |
| ) | |
| return data | |
| except Exception as e: | |
| logger.error(f"Error fetching data for {ticker}: {str(e)}") | |
| return None | |
| def get_current_cache_key(): | |
| """Generate cache key that expires every hour""" | |
| now = datetime.datetime.now() | |
| return f"{now.year}{now.month}{now.day}{now.hour}" | |
| def calculate_cagr(ticker, start_year, end_year): | |
| """Calculate CAGR for a ticker with error handling""" | |
| try: | |
| # Sanitize ticker | |
| clean_ticker = sanitize_ticker(ticker) | |
| if not clean_ticker: | |
| return f"- {ticker}: Invalid ticker format" | |
| # Validate years | |
| valid, msg = validate_year_range(start_year, end_year) | |
| if not valid: | |
| return f"- {clean_ticker}: {msg}" | |
| # Get cached data | |
| cache_key = get_current_cache_key() | |
| data = get_stock_data_cached( | |
| clean_ticker, | |
| f"{start_year}-01-01", | |
| f"{end_year}-12-31", | |
| cache_key | |
| ) | |
| if data is None: | |
| return f"- {clean_ticker}: Error fetching data (API timeout or network issue)" | |
| if data.empty: | |
| return f"- {clean_ticker}: No historical data available between {start_year} and {end_year}" | |
| # Handle MultiIndex columns | |
| if isinstance(data.columns, pd.MultiIndex): | |
| data.columns = data.columns.droplevel(1) | |
| # Check for Adj Close column | |
| if 'Adj Close' not in data.columns: | |
| return f"- {clean_ticker}: Adjusted Close price data not available" | |
| # Calculate CAGR | |
| initial = data['Adj Close'].iloc[0] | |
| final = data['Adj Close'].iloc[-1] | |
| if pd.isna(initial) or pd.isna(final): | |
| return f"- {clean_ticker}: Missing price data" | |
| if initial <= 0 or final <= 0: | |
| return f"- {clean_ticker}: Invalid price data (negative or zero values)" | |
| start_date = data.index[0] | |
| end_date = data.index[-1] | |
| days = (end_date - start_date).days | |
| years = days / 365.25 | |
| if years <= 0: | |
| return f"- {clean_ticker}: Invalid time period" | |
| cagr = ((final / initial) ** (1 / years) - 1) * 100 | |
| # Add context about data quality | |
| actual_start = start_date.strftime('%Y-%m-%d') | |
| actual_end = end_date.strftime('%Y-%m-%d') | |
| date_note = f" (data: {actual_start} to {actual_end})" if actual_start != f"{start_year}-01-01" else "" | |
| return f"- {clean_ticker}: ~{cagr:.2f}%{date_note}" | |
| except Exception as e: | |
| logger.error(f"Unexpected error calculating CAGR for {ticker}: {str(e)}") | |
| return f"- {ticker}: Calculation error - {str(e)}" | |
| def generate( | |
| message: str, | |
| chat_history: list[dict], | |
| system_prompt: str = DEFAULT_SYSTEM_PROMPT, | |
| max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS, | |
| temperature: float = 0.6, | |
| top_p: float = 0.9, | |
| top_k: int = 50, | |
| repetition_penalty: float = 1.2, | |
| ) -> Iterator[str]: | |
| start_time = time.time() | |
| logger.info(f"Generating response for message: {message[:100]}...") | |
| lower_message = message.lower().strip() | |
| # Quick responses for common queries | |
| if lower_message in ["hi", "hello", "hey"]: | |
| response = "Hello! I'm FinChat, your financial advisor. Ask me anything about investing, stocks, CAGR, compound interest, or portfolio analysis!" | |
| logger.info(f"Quick response generated in {time.time() - start_time:.2f}s") | |
| yield response | |
| return | |
| if "what is cagr" in lower_message: | |
| response = """- CAGR stands for Compound Annual Growth Rate. | |
| - It measures the mean annual growth rate of an investment over a specified period longer than one year, accounting for compounding. | |
| - Formula: CAGR = (Ending Value / Beginning Value)^(1 / Number of Years) - 1 | |
| - Useful for comparing investments over time. | |
| - Past performance is not indicative of future results. Consult a financial advisor.""" | |
| logger.info(f"Quick response generated in {time.time() - start_time:.2f}s") | |
| yield response | |
| return | |
| # Compound interest calculation | |
| compound_match = COMPOUND_INTEREST_PATTERN.search(lower_message) | |
| if compound_match: | |
| try: | |
| principal_str = compound_match.group(1).replace(',', '') | |
| principal = float(principal_str) | |
| rate = float(compound_match.group(2)) / 100 | |
| years = int(compound_match.group(3)) | |
| if principal <= 0: | |
| yield "Error: Principal amount must be positive." | |
| return | |
| if rate < 0 or rate > 1: | |
| yield "Error: Interest rate must be between 0% and 100%." | |
| return | |
| if years <= 0 or years > 100: | |
| yield "Error: Years must be between 1 and 100." | |
| return | |
| balance = principal * (1 + rate) ** years | |
| total_interest = balance - principal | |
| response = ( | |
| f"**Compound Interest Calculation**\n\n" | |
| f"- Starting Principal: ${principal:,.2f}\n" | |
| f"- Annual Interest Rate: {rate*100:.2f}%\n" | |
| f"- Time Period: {years} years\n" | |
| f"- Compounding: Annually\n\n" | |
| f"**Results:**\n" | |
| f"- Final Balance (Year {years}): ${balance:,.2f}\n" | |
| f"- Total Interest Earned: ${total_interest:,.2f}\n" | |
| f"- Total Growth: {((balance/principal - 1) * 100):.2f}%\n\n" | |
| f"*Note: This assumes annual compounding with no additional deposits or withdrawals. Actual results may vary. Consult a financial advisor.*" | |
| ) | |
| logger.info(f"Compound interest calculated in {time.time() - start_time:.2f}s") | |
| yield response | |
| return | |
| except ValueError as ve: | |
| logger.error(f"Error parsing compound interest query: {str(ve)}") | |
| yield "Error: Please ensure amount, rate, and years are valid numbers. Example: 'If I save $10000 at 5% interest over 10 years'" | |
| return | |
| except Exception as e: | |
| logger.error(f"Unexpected error in compound interest: {str(e)}") | |
| yield f"Error calculating compound interest: {str(e)}" | |
| return | |
| # CAGR calculation with improved pattern matching | |
| cagr_match = CAGR_PATTERN.search(lower_message) | |
| if cagr_match: | |
| tickers_str, start_year, end_year = cagr_match.groups() | |
| tickers = [t.strip() for t in re.split(r',|\band\b', tickers_str) if t.strip()] | |
| # Apply company-to-ticker mapping | |
| mapped_tickers = [] | |
| for ticker in tickers: | |
| lower_ticker = ticker.lower() | |
| if lower_ticker in COMPANY_TO_TICKER: | |
| mapped_tickers.append(COMPANY_TO_TICKER[lower_ticker]) | |
| else: | |
| mapped_tickers.append(ticker.upper()) | |
| # Validate year range first | |
| valid, msg = validate_year_range(start_year, end_year) | |
| if not valid: | |
| yield f"Error: {msg}" | |
| return | |
| if len(mapped_tickers) > 10: | |
| yield "Error: Too many tickers requested. Please limit to 10 tickers per query." | |
| return | |
| responses = [] | |
| for ticker in mapped_tickers: | |
| result = calculate_cagr(ticker, start_year, end_year) | |
| responses.append(result) | |
| full_response = ( | |
| f"**CAGR Analysis ({start_year} - {end_year})**\n\n" | |
| + "\n".join(responses) + | |
| "\n\n*Notes:*\n" | |
| "- CAGR represents average annual returns with compounding\n" | |
| "- Based on adjusted closing prices\n" | |
| "- Past performance is not indicative of future results\n" | |
| "- Please consult a financial advisor for investment decisions" | |
| ) | |
| logger.info(f"CAGR response generated in {time.time() - start_time:.2f}s") | |
| yield full_response | |
| return | |
| # Build conversation for LLM | |
| conversation = [{"role": "system", "content": system_prompt}] | |
| for msg in chat_history[-3:]: | |
| if msg["role"] in ["user", "assistant"]: | |
| conversation.append({"role": msg["role"], "content": msg["content"]}) | |
| conversation.append({"role": "user", "content": message}) | |
| # Token length check with truncation | |
| prompt_text = "\n".join(d["content"] for d in conversation) | |
| input_tokens = llm.tokenize(prompt_text.encode("utf-8"), add_bos=False) | |
| while len(input_tokens) > MAX_INPUT_TOKEN_LENGTH: | |
| logger.warning(f"Input tokens ({len(input_tokens)}) exceed limit. Truncating history.") | |
| if len(conversation) > 2: | |
| conversation.pop(1) | |
| prompt_text = "\n".join(d["content"] for d in conversation) | |
| input_tokens = llm.tokenize(prompt_text.encode("utf-8"), add_bos=False) | |
| else: | |
| yield "Error: Input is too long. Please shorten your query or start a new conversation." | |
| return | |
| # Generate response | |
| try: | |
| response = "" | |
| sentence_buffer = "" | |
| stream = llm.create_chat_completion( | |
| messages=conversation, | |
| max_tokens=max_new_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| top_k=top_k, | |
| repeat_penalty=repetition_penalty, | |
| stream=True | |
| ) | |
| sentence_endings = ['.', '!', '?'] | |
| for chunk in stream: | |
| delta = chunk["choices"][0]["delta"] | |
| if "content" in delta and delta["content"] is not None: | |
| cleaned_chunk = re.sub( | |
| r'<\|(?:im_start|im_end|system|user|assistant)\|>|</s>|\[END\]', | |
| '', | |
| delta["content"] | |
| ) | |
| if not cleaned_chunk: | |
| continue | |
| sentence_buffer += cleaned_chunk | |
| response += cleaned_chunk | |
| if any(sentence_buffer.strip().endswith(ending) for ending in sentence_endings): | |
| yield response | |
| sentence_buffer = "" | |
| if chunk["choices"][0]["finish_reason"] is not None: | |
| if sentence_buffer.strip(): | |
| yield response | |
| break | |
| duration = time.time() - start_time | |
| logger.info(f"LLM response generated in {duration:.2f}s") | |
| except ValueError as ve: | |
| if "exceed context window" in str(ve): | |
| yield "Error: Input exceeds context window. Please try a shorter query." | |
| else: | |
| logger.error(f"ValueError during generation: {str(ve)}") | |
| yield f"Error: {str(ve)}" | |
| except Exception as e: | |
| logger.error(f"Error during response generation: {str(e)}") | |
| yield f"Error generating response. Please try again or rephrase your question." | |
| def process_portfolio(df, growth_rate): | |
| """Process portfolio with enhanced error handling and validation""" | |
| if df is None or len(df) == 0: | |
| return "", None | |
| try: | |
| if not isinstance(df, pd.DataFrame): | |
| df = pd.DataFrame(df, columns=["Ticker", "Shares", "Avg Cost", "Current Price"]) | |
| # Validate and convert numeric columns | |
| for col in ["Shares", "Avg Cost", "Current Price"]: | |
| df[col] = pd.to_numeric(df[col], errors='coerce') | |
| df = df.dropna(subset=["Ticker"]) | |
| portfolio = {} | |
| errors = [] | |
| for idx, row in df.iterrows(): | |
| ticker = sanitize_ticker(row["Ticker"]) if pd.notna(row["Ticker"]) else None | |
| if not ticker: | |
| continue | |
| shares = float(row["Shares"]) if pd.notna(row["Shares"]) else 0 | |
| cost = float(row["Avg Cost"]) if pd.notna(row["Avg Cost"]) else 0 | |
| price = float(row["Current Price"]) if pd.notna(row["Current Price"]) else 0 | |
| if shares <= 0: | |
| errors.append(f"{ticker}: Invalid shares count") | |
| continue | |
| if price < 0 or cost < 0: | |
| errors.append(f"{ticker}: Negative prices not allowed") | |
| continue | |
| value = shares * price | |
| cost_basis = shares * cost | |
| gain_loss = value - cost_basis | |
| gain_loss_pct = (gain_loss / cost_basis * 100) if cost_basis > 0 else 0 | |
| portfolio[ticker] = { | |
| 'shares': shares, | |
| 'cost': cost, | |
| 'price': price, | |
| 'value': value, | |
| 'cost_basis': cost_basis, | |
| 'gain_loss': gain_loss, | |
| 'gain_loss_pct': gain_loss_pct | |
| } | |
| if not portfolio: | |
| return "No valid portfolio entries found. Please check your data.", None | |
| total_value_now = sum(v['value'] for v in portfolio.values()) | |
| total_cost_basis = sum(v['cost_basis'] for v in portfolio.values()) | |
| total_gain_loss = total_value_now - total_cost_basis | |
| total_gain_loss_pct = (total_gain_loss / total_cost_basis * 100) if total_cost_basis > 0 else 0 | |
| allocations = {k: v['value'] / total_value_now for k, v in portfolio.items()} if total_value_now > 0 else {} | |
| # Create allocation pie chart | |
| fig_alloc, ax_alloc = plt.subplots(figsize=(8, 6)) | |
| colors = plt.cm.Set3(range(len(allocations))) | |
| ax_alloc.pie( | |
| allocations.values(), | |
| labels=allocations.keys(), | |
| autopct='%1.1f%%', | |
| colors=colors, | |
| startangle=90 | |
| ) | |
| ax_alloc.set_title('Portfolio Allocation by Value', fontsize=14, fontweight='bold') | |
| buf_alloc = io.BytesIO() | |
| fig_alloc.savefig(buf_alloc, format='png', bbox_inches='tight', dpi=100) | |
| buf_alloc.seek(0) | |
| chart_alloc = Image.open(buf_alloc) | |
| plt.close(fig_alloc) | |
| # Project future values | |
| def project_value(value, years, rate): | |
| return value * (1 + rate / 100) ** years | |
| projections = { | |
| '1 year': sum(project_value(v['value'], 1, growth_rate) for v in portfolio.values()), | |
| '2 years': sum(project_value(v['value'], 2, growth_rate) for v in portfolio.values()), | |
| '5 years': sum(project_value(v['value'], 5, growth_rate) for v in portfolio.values()), | |
| '10 years': sum(project_value(v['value'], 10, growth_rate) for v in portfolio.values()) | |
| } | |
| # Build detailed report | |
| data_str = "**📊 Portfolio Analysis**\n\n" | |
| data_str += "**Current Holdings:**\n" | |
| for ticker, data in portfolio.items(): | |
| data_str += ( | |
| f"- {ticker}: {data['shares']:.2f} shares @ ${data['price']:.2f} " | |
| f"(Cost: ${data['cost']:.2f}) = ${data['value']:,.2f} " | |
| f"[{data['gain_loss_pct']:+.2f}%]\n" | |
| ) | |
| data_str += f"\n**Portfolio Summary:**\n" | |
| data_str += f"- Total Value: ${total_value_now:,.2f}\n" | |
| data_str += f"- Total Cost Basis: ${total_cost_basis:,.2f}\n" | |
| data_str += f"- Total Gain/Loss: ${total_gain_loss:+,.2f} ({total_gain_loss_pct:+.2f}%)\n" | |
| data_str += f"\n**Projected Values (at {growth_rate}% annual growth):**\n" | |
| for period, value in projections.items(): | |
| gain = value - total_value_now | |
| data_str += f"- {period}: ${value:,.2f} (+${gain:,.2f})\n" | |
| if errors: | |
| data_str += f"\n**⚠️ Warnings:**\n" | |
| for error in errors: | |
| data_str += f"- {error}\n" | |
| data_str += "\n*Note: Projections assume constant growth rate and no additional contributions. Actual results will vary. Consult a financial advisor.*" | |
| return data_str, chart_alloc | |
| except Exception as e: | |
| logger.error(f"Error processing portfolio: {str(e)}") | |
| return f"Error processing portfolio: {str(e)}", None | |
| def fetch_current_prices(df): | |
| """Fetch current prices with timeout and error handling""" | |
| if df is None or len(df) == 0: | |
| return df | |
| try: | |
| if not isinstance(df, pd.DataFrame): | |
| df = pd.DataFrame(df, columns=["Ticker", "Shares", "Avg Cost", "Current Price"]) | |
| updated_count = 0 | |
| failed_tickers = [] | |
| for i in df.index: | |
| ticker = df.at[i, "Ticker"] | |
| if pd.notna(ticker) and ticker.strip(): | |
| clean_ticker = sanitize_ticker(ticker) | |
| if not clean_ticker: | |
| failed_tickers.append(f"{ticker} (invalid format)") | |
| continue | |
| try: | |
| ticker_obj = yf.Ticker(clean_ticker) | |
| info = ticker_obj.info | |
| price = info.get('currentPrice') or info.get('regularMarketPrice') | |
| if price is not None and price > 0: | |
| df.at[i, "Current Price"] = price | |
| updated_count += 1 | |
| else: | |
| failed_tickers.append(f"{clean_ticker} (no price data)") | |
| except Exception as e: | |
| logger.warning(f"Failed to fetch price for {clean_ticker}: {str(e)}") | |
| failed_tickers.append(f"{clean_ticker} ({str(e)[:30]})") | |
| if updated_count > 0: | |
| logger.info(f"Successfully updated {updated_count} prices") | |
| if failed_tickers: | |
| logger.warning(f"Failed to fetch: {', '.join(failed_tickers)}") | |
| return df | |
| except Exception as e: | |
| logger.error(f"Error in fetch_current_prices: {str(e)}") | |
| return df | |
| # Gradio interface | |
| with gr.Blocks(theme=themes.Soft(), css=""" | |
| #chatbot {height: 800px; overflow: auto;} | |
| .performance-note {color: #666; font-size: 0.9em; font-style: italic;} | |
| """) as demo: | |
| gr.Markdown(DESCRIPTION) | |
| chatbot = gr.Chatbot(label="FinChat", type="messages") | |
| msg = gr.Textbox( | |
| label="Ask a finance question", | |
| placeholder="e.g., 'What is CAGR?' or 'Average return for AAPL between 2010 and 2020'", | |
| info="Enter your query. Responses are cached for better performance." | |
| ) | |
| with gr.Row(): | |
| submit = gr.Button("Submit", variant="primary") | |
| clear = gr.Button("Clear") | |
| gr.Examples( | |
| examples=[ | |
| "What is CAGR?", | |
| "Average return for AAPL between 2015 and 2023", | |
| "Average return for TSLA and NVDA between 2018 and 2023", | |
| "If I save $10000 at 5% interest over 10 years", | |
| "Explain compound interest" | |
| ], | |
| inputs=msg, | |
| label="Example Queries" | |
| ) | |
| with gr.Accordion("📈 Enter Portfolio for Projections", open=False): | |
| portfolio_df = gr.Dataframe( | |
| headers=["Ticker", "Shares", "Avg Cost", "Current Price"], | |
| datatype=["str", "number", "number", "number"], | |
| row_count=5, | |
| col_count=(4, "fixed"), | |
| label="Portfolio Data", | |
| interactive=True | |
| ) | |
| gr.Markdown(""" | |
| **Instructions:** | |
| - Enter stock tickers (e.g., AAPL, TSLA) | |
| - Fill in number of shares and your average cost per share | |
| - Click 'Fetch Current Prices' to auto-populate current prices | |
| - Adjust growth rate for future projections | |
| """) | |
| fetch_button = gr.Button("🔄 Fetch Current Prices", variant="secondary") | |
| fetch_button.click(fetch_current_prices, inputs=portfolio_df, outputs=portfolio_df) | |
| growth_rate = gr.Slider( | |
| minimum=0, | |
| maximum=50, | |
| step=1, | |
| value=10, | |
| label="Annual Growth Rate (%)", | |
| interactive=True, | |
| info="Expected annual return for projections (0-50%)" | |
| ) | |
| growth_rate_label = gr.Markdown("**Selected Growth Rate: 10%**") | |
| with gr.Accordion("⚙️ Advanced Settings", open=False): | |
| system_prompt = gr.Textbox( | |
| label="System Prompt", | |
| value=DEFAULT_SYSTEM_PROMPT, | |
| lines=6, | |
| info="Customize the AI's behavior" | |
| ) | |
| temperature = gr.Slider( | |
| label="Temperature", | |
| value=0.6, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.05, | |
| info="Lower = more focused, Higher = more creative" | |
| ) | |
| top_p = gr.Slider( | |
| label="Top P", | |
| value=0.9, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.05, | |
| info="Nucleus sampling threshold" | |
| ) | |
| top_k = gr.Slider( | |
| label="Top K", | |
| value=50, | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| info="Limit to top K tokens" | |
| ) | |
| repetition_penalty = gr.Slider( | |
| label="Repetition Penalty", | |
| value=1.2, | |
| minimum=1.0, | |
| maximum=2.0, | |
| step=0.05, | |
| info="Penalize repeated tokens" | |
| ) | |
| max_new_tokens = gr.Slider( | |
| label="Max New Tokens", | |
| value=DEFAULT_MAX_NEW_TOKENS, | |
| minimum=1, | |
| maximum=MAX_MAX_NEW_TOKENS, | |
| step=1, | |
| info="Maximum length of generated response" | |
| ) | |
| gr.Markdown(LICENSE) | |
| gr.Markdown('<p class="performance-note">⚡ Performance optimized with caching and improved error handling</p>') | |
| def update_growth_rate_label(growth_rate): | |
| return f"**Selected Growth Rate: {growth_rate}%**" | |
| def user(message, history): | |
| if not message: | |
| return "", history | |
| return "", history + [{"role": "user", "content": message}] | |
| def bot(history, sys_prompt, temp, tp, tk, rp, mnt, portfolio_df, growth_rate): | |
| if not history: | |
| logger.warning("History is empty, initializing with user message.") | |
| history = [{"role": "user", "content": ""}] | |
| message = history[-1]["content"] | |
| portfolio_data, chart_alloc = process_portfolio(portfolio_df, growth_rate) | |
| if portfolio_data: | |
| message += "\n\n" + portfolio_data | |
| history[-1]["content"] = message | |
| history.append({"role": "assistant", "content": ""}) | |
| for new_text in generate(message, history[:-1], sys_prompt, mnt, temp, tp, tk, rp): | |
| history[-1]["content"] = new_text | |
| yield history, f"**Selected Growth Rate: {growth_rate}%**" | |
| if chart_alloc: | |
| # Append chart as a separate message | |
| yield history, f"**Selected Growth Rate: {growth_rate}%**" | |
| growth_rate.change(update_growth_rate_label, inputs=growth_rate, outputs=growth_rate_label) | |
| submit.click( | |
| user, | |
| [msg, chatbot], | |
| [msg, chatbot], | |
| queue=False | |
| ).then( | |
| bot, | |
| [chatbot, system_prompt, temperature, top_p, top_k, repetition_penalty, max_new_tokens, portfolio_df, growth_rate], | |
| [chatbot, growth_rate_label] | |
| ) | |
| msg.submit( | |
| user, | |
| [msg, chatbot], | |
| [msg, chatbot], | |
| queue=False | |
| ).then( | |
| bot, | |
| [chatbot, system_prompt, temperature, top_p, top_k, repetition_penalty, max_new_tokens, portfolio_df, growth_rate], | |
| [chatbot, growth_rate_label] | |
| ) | |
| clear.click(lambda: [], None, chatbot, queue=False) | |
| demo.queue(max_size=128).launch() |