Brancheneinstufung2/transcription-tool/backend/lib/gemini_client.py

import os
import logging
import random
import time
from functools import wraps

# --- KI UMSCHALTUNG: Google Generative AI (Dual Support) ---
# This is a simplified, self-contained version for the transcription tool.

HAS_NEW_GENAI = False
HAS_OLD_GENAI = False

# 1. New library (google-genai)
try:
    from google import genai
    from google.genai import types
    HAS_NEW_GENAI = True
    logging.info("Library 'google.genai' (v1.0+) loaded.")
except ImportError:
    logging.warning("Library 'google.genai' not found. Trying fallback.")

# 2. Old library (google-generativeai)
try:
    import google.generativeai as old_genai
    HAS_OLD_GENAI = True
    logging.info("Library 'google.generativeai' (Legacy) loaded.")
except ImportError:
    logging.warning("Library 'google.generativeai' not found.")

HAS_GEMINI = HAS_NEW_GENAI or HAS_OLD_GENAI

# A simple retry decorator, as the global one is not available
def retry_on_failure(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        max_retries = 3
        base_delay = 5
        for attempt in range(max_retries):
            try:
                if attempt > 0:
                    logging.warning(f"Retrying attempt {attempt + 1}/{max_retries} for '{func.__name__}'...")
                return func(*args, **kwargs)
            except Exception as e:
                if attempt < max_retries - 1:
                    wait_time = base_delay * (2 ** attempt) + random.uniform(0, 1)
                    time.sleep(wait_time)
                else:
                    raise e
    return wrapper

def _get_gemini_api_key():
    """Gets the Gemini API key from environment variables."""
    api_key = os.environ.get("GEMINI_API_KEY") or os.environ.get("OPENAI_API_KEY")
    if not api_key:
        raise ValueError("GEMINI_API_KEY or OPENAI_API_KEY environment variable not set.")
    return api_key

@retry_on_failure
def call_gemini_flash(prompt: str, system_instruction: str = None, temperature: float = 0.3, json_mode: bool = False):
    """
    Calls the Gemini Flash model to generate text content.
    This is a focused, local version of the function.
    """
    logger = logging.getLogger(__name__)
    api_key = _get_gemini_api_key()

    if not HAS_GEMINI:
        raise ImportError("No Google Generative AI library is available (google-genai or google-generativeai).")

    # The legacy library was noted as preferred in the original helpers.py
    if HAS_OLD_GENAI:
        try:
            old_genai.configure(api_key=api_key)
            generation_config = {
                "temperature": temperature,
                "top_p": 0.95,
                "top_k": 40,
                "max_output_tokens": 8192,
            }
            if json_mode:
                generation_config["response_mime_type"] = "application/json"
            
            model = old_genai.GenerativeModel(
                model_name="gemini-1.5-flash", # Using 1.5 as it's the modern standard
                generation_config=generation_config,
                system_instruction=system_instruction
            )
            response = model.generate_content([prompt])
            return response.text.strip()
        except Exception as e:
            logger.error(f"Error with legacy GenAI Lib: {e}")
            if not HAS_NEW_GENAI: raise e
            # Fallthrough to new lib if legacy fails

    # Fallback to the new library
    if HAS_NEW_GENAI:
        try:
            # CORRECT: Use the Client-based API for the new library
            client = genai.Client(api_key=api_key)
            
            config = {
                "temperature": temperature,
                "top_p": 0.95,
                "top_k": 40,
                "max_output_tokens": 8192,
            }
            if json_mode:
                config["response_mime_type"] = "application/json"

            # Construct the contents list, including the system instruction if provided
            contents = []
            if system_instruction:
                # Note: The new API doesn't have a direct 'system_instruction' parameter
                # in generate_content. It's typically passed as the first message.
                # This is an adaptation. For a more robust solution, one would
                # structure prompts with roles.
                contents.append({'role': 'system', 'parts': [{'text': system_instruction}]})
            contents.append({'role': 'user', 'parts': [{'text': prompt}]})
            
            # Use the client to generate content
            response = client.models.generate_content(
                model="models/gemini-2.0-flash-001", # CORRECTED: Using the project's standard model
                contents=contents,
                config=config
            )
            return response.text.strip()
            
        except Exception as e:
            logger.error(f"Error with new GenAI Lib: {e}")
            raise e
            
    raise RuntimeError("Both Gemini libraries failed or are unavailable.")