99 lines
4.5 KiB
Python
99 lines
4.5 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.generativeai as genai
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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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@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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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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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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model = genai.GenerativeModel(model_name="gemini-1.5-pro-latest",
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generation_config=generation_config,
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system_instruction=system_prompt)
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try:
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# text_response = []
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response = model.generate_content(prompt, stream=True)
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if response:
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for chunk in response:
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# text_response.append(chunk.text)
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print(chunk.text)
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else:
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print(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 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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