# chatbot_handler.py import logging import json from google import genai from google.genai import types as genai_types # Import types for GenerateContentConfig import os import asyncio # Gemini API key configuration GEMINI_API_KEY = os.getenv('GEMINI_API_KEY', '') client = None model_name = "gemini-2.0-flash" # As per user's documentation snippet, ensure this model is available with their API key type # This will be used to create genai_types.GenerateContentConfig generation_config_params = { "temperature": 0.7, "top_p": 1, "top_k": 1, "max_output_tokens": 2048, # If you need a system instruction, add it here, e.g.: # "system_instruction": "You are a helpful AI assistant providing insights on LinkedIn analytics." } try: if GEMINI_API_KEY: # Initialize client using genai.Client client = genai.Client(api_key=GEMINI_API_KEY) logging.info(f"Gemini client (genai.Client) initialized. Target model for generation: '{model_name}'") else: logging.error("Gemini API Key is not set.") except Exception as e: logging.error(f"Failed to initialize Gemini client (genai.Client): {e}", exc_info=True) def format_history_for_gemini(gradio_chat_history: list) -> list: """Converts Gradio chat history to Gemini content format.""" gemini_contents = [] for msg in gradio_chat_history: role = "user" if msg.get("role") == "user" else "model" content = msg.get("content") if isinstance(content, str): gemini_contents.append({"role": role, "parts": [{"text": content}]}) elif isinstance(content, list) and len(content) > 0 and isinstance(content[0], dict) and "type" in content[0]: parts = [] for part_item in content: if part_item.get("type") == "text": parts.append({"text": part_item.get("text", "")}) if parts: gemini_contents.append({"role": role, "parts": parts}) else: logging.warning(f"Skipping complex but empty content part in chat history: {content}") else: logging.warning(f"Skipping non-string/non-standard content in chat history: {content}") # For `client.models.generate_content`, the `contents` parameter # expects a list of `Content` objects (or dicts that can be cast to them). # Each `Content` object has 'role' and 'parts'. return gemini_contents async def generate_llm_response(user_message: str, plot_id: str, plot_label: str, chat_history_for_plot: list, plot_data_summary: str = None): if not client: logging.error("Gemini client (genai.Client) not initialized.") return "The AI model is not available. Configuration error." gemini_formatted_history = format_history_for_gemini(chat_history_for_plot) if not gemini_formatted_history: logging.error("Formatted history for Gemini is empty.") return "There was an issue processing the conversation history (empty)." if not any(part.get("text","").strip() for message in gemini_formatted_history for part in message.get("parts",[])): logging.error("Formatted history for Gemini contains no text parts.") return "There was an issue processing the conversation history for the AI model (empty text)." try: response = None if hasattr(client, 'models') and hasattr(client.models, 'generate_content'): logging.debug(f"Using genai.Client.models.generate_content for model '{model_name}' (synchronous via asyncio.to_thread)") # Create the GenerateContentConfig object from our parameters # This can include system_instruction if added to generation_config_params gen_config_obj = genai_types.GenerateContentConfig(**generation_config_params) # Call client.models.generate_content # 1. Use model_name directly (e.g., "gemini-1.5-flash-latest") # 2. Use 'config' instead of 'generation_config' for the keyword argument response = await asyncio.to_thread( client.models.generate_content, model=model_name, # Use model_name directly contents=gemini_formatted_history, config=gen_config_obj # Corrected keyword argument ) else: logging.error(f"Gemini client (genai.Client) does not have 'models.generate_content' method. Type: {type(client)}") return "AI model interaction error (SDK method not found)." # Process response if hasattr(response, 'prompt_feedback') and response.prompt_feedback and response.prompt_feedback.block_reason: reason = response.prompt_feedback.block_reason reason_name = getattr(reason, 'name', str(reason)) logging.warning(f"Blocked by prompt feedback: {reason_name}") return f"Blocked due to content policy: {reason_name}." if hasattr(response, 'text') and response.text: logging.debug("Response has a direct .text attribute.") return response.text logging.debug("Response does not have a direct .text attribute or it's empty, checking candidates.") if response.candidates and response.candidates[0].content and response.candidates[0].content.parts: return "".join(part.text for part in response.candidates[0].content.parts if hasattr(part, 'text')) finish_reason = "UNKNOWN" if response.candidates and response.candidates[0].finish_reason: finish_reason_val = response.candidates[0].finish_reason finish_reason = getattr(finish_reason_val, 'name', str(finish_reason_val)) if not (hasattr(response, 'text') and response.text) and \ not (response.candidates and response.candidates[0].content and response.candidates[0].content.parts): logging.warning(f"No content parts in response and no direct .text. Finish reason: {finish_reason}") if finish_reason == "SAFETY": return f"Response generation stopped due to safety reasons. Finish reason: {finish_reason}." return f"The AI model returned an empty response. Finish reason: {finish_reason}." return f"Unexpected response structure from AI model (checked .text and .candidates). Finish reason: {finish_reason}." except AttributeError as ae: logging.error(f"AttributeError during Gemini call for plot '{plot_label}': {ae}", exc_info=True) return f"AI model error (Attribute): {type(ae).__name__} - {ae}." except Exception as e: logging.error(f"Error generating response for plot '{plot_label}': {e}", exc_info=True) if "API_KEY_INVALID" in str(e) or "API key not valid" in str(e): return "AI model error: API key is not valid. Please check configuration." if "400" in str(e) and "model" in str(e).lower() and "not found" in str(e).lower(): return f"AI model error: Model '{model_name}' not found or not accessible with your API key." # Check for the specific TypeError related to generate_content arguments if isinstance(e, TypeError) and "got an unexpected keyword argument" in str(e): logging.error(f"TypeError in generate_content call: {e}. This might indicate an issue with SDK version or method signature.") return f"AI model error (Internal SDK call issue): {e}" return f"An unexpected error occurred while contacting the AI model: {type(e).__name__}."