import os import google.generativeai as genai from dotenv import load_dotenv # Load environment variables load_dotenv() # Configure the Gemini API genai.configure(api_key=os.getenv('GOOGLE_API_KEY')) def generate_ai_response(prompt, model_name='gemini-pro', temperature=0.7, max_tokens=1000): """ Generate an AI response using Google's Generative AI (Gemini) Args: prompt (str): The input prompt for the AI model_name (str, optional): The Gemini model to use. Defaults to 'gemini-pro'. temperature (float, optional): Controls randomness. Defaults to 0.7. max_tokens (int, optional): Maximum length of the generated response. Defaults to 1000. Returns: str: The generated AI response """ try: # Select the model model = genai.GenerativeModel(model_name) # Generate content response = model.generate_content( prompt, generation_config=genai.types.GenerationConfig( temperature=temperature, max_output_tokens=max_tokens ) ) # Return the text of the response return response.text except Exception as e: print(f"Error generating AI response: {e}") return f"An error occurred while generating the response: {str(e)}" def simulate_ai_response(prompt): """ Simulated AI response for development and testing Args: prompt (str): The input prompt Returns: str: A simulated response based on the prompt """ # This is a placeholder for simulated responses during development # In a real implementation, this would be removed or more sophisticatedly implemented import random simulated_responses = [ "Based on the current financial data, here are some key insights...", "The AI suggests optimizing your spending in these key areas...", "Your startup shows promising growth potential with these recommendations...", "We've identified potential areas for financial improvement...", "Here's a strategic overview of your financial situation..." ] return random.choice(simulated_responses)