278 lines
11 KiB
Python
278 lines
11 KiB
Python
# Using Gemini Pro LLM model
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import os
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import sys
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from pathlib import Path
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import google.genai as genai
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from google.genai import types
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from dotenv import load_dotenv
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load_dotenv(Path('../../../.env'))
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from loguru import logger
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logger.remove()
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logger.add(sys.stdout,
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colorize=True,
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format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
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)
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from tenacity import (
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retry,
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stop_after_attempt,
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wait_random_exponential,
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)
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import asyncio
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import json
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import re
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from typing import Optional, Dict, Any
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# Configure standard logging
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import logging
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logging.basicConfig(level=logging.INFO, format='[%(asctime)s-%(levelname)s-%(module)s-%(lineno)d]- %(message)s')
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logger = logging.getLogger(__name__)
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@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
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def gemini_text_response(prompt, temperature, top_p, n, max_tokens, system_prompt):
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""" Common functiont to get response from gemini pro Text. """
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#FIXME: Include : https://github.com/google-gemini/cookbook/blob/main/quickstarts/rest/System_instructions_REST.ipynb
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try:
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client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
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except Exception as err:
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logger.error(f"Failed to configure Gemini: {err}")
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logger.info(f"Temp: {temperature}, MaxTokens: {max_tokens}, TopP: {top_p}, N: {n}")
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# Set up AI model config
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generation_config = {
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"temperature": temperature,
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"top_p": top_p,
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"top_k": n,
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"max_output_tokens": max_tokens,
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}
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# FIXME: Expose model_name in main_config
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try:
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response = client.models.generate_content(
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model='gemini-2.5-pro',
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contents=prompt,
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config=types.GenerateContentConfig(
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system_instruction=system_prompt,
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max_output_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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top_k=n,
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),
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)
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#logger.info(f"Number of Token in Prompt Sent: {model.count_tokens(prompt)}")
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return response.text
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except Exception as err:
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logger.error(f"Failed to get response from Gemini: {err}. Retrying.")
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#@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
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#def gemini_blog_metadata_json(blog_content):
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# """ Common functiont to get response from gemini pro Text. """
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# prompt = f"I will provide you with the content of a blog post. Based on this content, you need to generate the following elements in JSON format:\n\n1. **Blog Title**: A compelling and relevant title that summarizes the blog content.\n2. **Meta Description**: A concise meta description (up to 160 characters) that captures the essence of the blog post and encourages clicks.\n3. **Tags**: A list of 5-10 relevant tags that represent the key topics covered in the blog post.\n4. **Categories**: A list of 1-3 appropriate categories that best describe the blog post's main themes.\n\nOutput your response in the following JSON format:\n\n```json\n{\n \"type\": \"object\",\n \"properties\": {\n \"blog_title\": {\n \"type\": \"string\"\n },\n \"meta_description\": {\n \"type\": \"string\"\n },\n \"tags\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"categories\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n }\n }\n}\n\n. The Blog Content is given below: \n\n{blog_content}\n\n"
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#
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# try:
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# genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
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# except Exception as err:
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# logger.error(f"Failed to configure Gemini: {err}")
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#
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# # Create the model
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# generation_config = {
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# "temperature": 1,
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# "top_p": 0.95,
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# "top_k": 64,
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# "max_output_tokens": 8192,
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# "response_schema": content.Schema(
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# type = content.Type.OBJECT,
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# properties = {
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# "response": content.Schema(
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# type = content.Type.STRING,
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# ),
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# },
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# ),
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# "response_mime_type": "application/json",
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# }
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#
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# model = genai.GenerativeModel(
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# model_name="gemini-1.5-flash",
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# generation_config=generation_config,
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# # safety_settings = Adjust safety settings
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# # See https://ai.google.dev/gemini-api/docs/safety-settings
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# )
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#
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# try:
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# # text_response = []
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# response = model.generate_content(prompt)
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# if response:
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# logger.info(f"Number of Token in Prompt Sent: {model.count_tokens(prompt)}")
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# return response.text
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# except Exception as err:
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# logger.error(f"Failed to get SEO METADATA from Gemini: {err}. Retrying.")
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async def test_gemini_api_key(api_key: str) -> tuple[bool, str]:
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"""
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Test if the provided Gemini API key is valid.
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Args:
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api_key (str): The Gemini API key to test
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Returns:
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tuple[bool, str]: A tuple containing (is_valid, message)
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"""
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try:
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# Configure Gemini with the provided key
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genai.configure(api_key=api_key)
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# Try to list models as a simple API test
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models = genai.list_models()
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# Check if Gemini Pro is available
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if any(model.name == "gemini-pro" for model in models):
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return True, "Gemini API key is valid"
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else:
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return False, "Gemini Pro model not available with this API key"
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except Exception as e:
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return False, f"Error testing Gemini API key: {str(e)}"
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def gemini_pro_text_gen(prompt, temperature=0.7, top_p=0.9, top_k=40, max_tokens=2048):
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"""
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Generate text using Google's Gemini Pro model.
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Args:
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prompt (str): The input text to generate completion for
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temperature (float, optional): Controls randomness. Defaults to 0.7
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top_p (float, optional): Controls diversity. Defaults to 0.9
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top_k (int, optional): Controls vocabulary size. Defaults to 40
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max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 2048
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Returns:
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str: The generated text completion
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"""
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try:
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# Configure the model
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model = genai.GenerativeModel('gemini-pro')
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# Generate content
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response = model.generate_content(
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prompt,
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generation_config=genai.types.GenerationConfig(
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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max_output_tokens=max_tokens,
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)
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)
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# Return the generated text
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return response.text
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except Exception as e:
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logger.error(f"Error in Gemini Pro text generation: {e}")
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return str(e)
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def _dict_to_types_schema(schema: Dict[str, Any]) -> types.Schema:
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"""Convert a lightweight dict schema to google.genai.types.Schema."""
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if not isinstance(schema, dict):
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raise ValueError("response_schema must be a dict compatible with types.Schema")
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def _convert(node: Dict[str, Any]) -> types.Schema:
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node_type = (node.get("type") or "OBJECT").upper()
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if node_type == "OBJECT":
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props = node.get("properties") or {}
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props_types: Dict[str, types.Schema] = {}
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for key, prop in props.items():
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if isinstance(prop, dict):
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props_types[key] = _convert(prop)
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else:
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props_types[key] = types.Schema(type=types.Type.STRING)
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return types.Schema(type=types.Type.OBJECT, properties=props_types if props_types else None)
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elif node_type == "ARRAY":
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items_node = node.get("items")
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if isinstance(items_node, dict):
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item_schema = _convert(items_node)
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else:
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item_schema = types.Schema(type=types.Type.STRING)
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return types.Schema(type=types.Type.ARRAY, items=item_schema)
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elif node_type == "NUMBER":
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return types.Schema(type=types.Type.NUMBER)
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elif node_type == "BOOLEAN":
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return types.Schema(type=types.Type.BOOLEAN)
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else:
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return types.Schema(type=types.Type.STRING)
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return _convert(schema)
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@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
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def gemini_structured_json_response(prompt, schema, temperature=0.7, top_p=0.9, top_k=40, max_tokens=2048, system_prompt=None):
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"""
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Generate structured JSON response using Google's Gemini Pro model.
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"""
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try:
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client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
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# Build config using official SDK schema type
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try:
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types_schema = _dict_to_types_schema(schema) if isinstance(schema, dict) else schema
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except Exception as conv_err:
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logger.warning(f"Schema conversion warning, defaulting to OBJECT: {conv_err}")
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types_schema = types.Schema(type=types.Type.OBJECT)
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generation_config = types.GenerateContentConfig(
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system_instruction=system_prompt,
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max_output_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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response_mime_type='application/json',
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response_schema=types_schema
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)
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response = client.models.generate_content(
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model='gemini-2.5-flash',
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contents=prompt,
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config=generation_config,
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)
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# Prefer parsed if present and non-empty; otherwise parse text with fallbacks
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try:
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parsed = getattr(response, 'parsed', None)
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if parsed:
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return parsed if isinstance(parsed, dict) else json.loads(json.dumps(parsed))
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text = (response.text or '').strip()
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# Strip markdown code fences if present
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if text.startswith('```'):
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# remove leading ```json or ``` and trailing ```
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if text.lower().startswith('```json'):
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text = text[7:]
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else:
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text = text[3:]
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if text.endswith('```'):
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text = text[:-3]
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text = text.strip()
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try:
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return json.loads(text)
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except json.JSONDecodeError:
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# Fallback: extract likely JSON object substring
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first = text.find('{')
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last = text.rfind('}')
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if first != -1 and last != -1 and last > first:
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candidate = text[first:last+1]
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try:
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return json.loads(candidate)
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except json.JSONDecodeError:
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pass
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# Final fallback: regex any object
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import re
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match = re.search(r'\{[\s\S]*\}', text)
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if match:
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return json.loads(match.group(0))
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raise
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except Exception as e:
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logger.error(f"Error parsing structured response: {e}")
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return {"error": f"Failed to parse JSON response: {e}", "raw_response": (response.text or '')}
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except Exception as e:
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logger.error(f"Error in Gemini Pro structured JSON generation: {e}")
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return {"error": str(e)} |