114 lines
4.9 KiB
Python
114 lines
4.9 KiB
Python
import sys
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import os
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from textwrap import dedent
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from PIL import Image
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import json
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import asyncio
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from pathlib import Path
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from datetime import datetime
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import streamlit as st
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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 ..ai_web_researcher.firecrawl_web_crawler import scrape_url
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from ..blog_metadata.get_blog_metadata import blog_metadata
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from ..blog_postprocessing.save_blog_to_file import save_blog_to_file
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from ..gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
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from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
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import google.generativeai as genai
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def blog_from_image(prompt, uploaded_img):
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"""
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This function will take a blog Topic to first generate sections for it
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and then generate content for each section.
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"""
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# Use to store the blog in a string, to save in a *.md file.
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blog_markdown_str = None
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logger.info(f"Researching and Writing Blog on {uploaded_img} and {prompt}")
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# FIXME: Implement support for Openai.
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if not os.getenv("GEMINI_API_KEY"):
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st.error("Only Gemini supported, Open Issue ticket on github for Openai, others.")
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st.stop()
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with st.status("Started Writing from Image..", expanded=True) as status:
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st.empty()
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status.update(label=f"Researching and Writing Blog on given Image")
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try:
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blog_markdown_str = write_blog_from_image(prompt, uploaded_img)
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except Exception as err:
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st.error(f"Failed to write blog from Image - Error: {err}")
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logger.error(f"Failed to write blog from image: {err}")
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st.stop()
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status.update(label="Successfully wrote blog from image.", expanded=False, state="complete")
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try:
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status.update(label="🙎 Generating - Title, Meta Description, Tags, Categories for the content.")
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blog_title, blog_meta_desc, blog_tags, blog_categories = asyncio.run(blog_metadata(blog_markdown_str))
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except Exception as err:
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st.error(f"Failed to get blog metadata: {err}")
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try:
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status.update(label="🙎 Generating Image for the new blog.")
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generated_image_filepath = generate_image(f"{blog_title} + ' ' + {blog_meta_desc}")
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except Exception as err:
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st.warning(f"Failed in Image generation: {err}")
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saved_blog_to_file = save_blog_to_file(blog_markdown_str, blog_title, blog_meta_desc,
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blog_tags, blog_categories, generated_image_filepath)
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status.update(label=f"Saved the content in this file: {saved_blog_to_file}")
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logger.info(f"\n\n --------- Finished writing Blog -------------- \n")
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st.image(generated_image_filepath, caption=blog_title)
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st.markdown(f"{blog_markdown_str}")
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status.update(label=f"Finished, Review & Use your Original Content Below: {saved_blog_to_file}", state="complete")
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# Clean up the temporary file after processing (optional)
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os.remove(uploaded_img)
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def write_blog_from_image(prompt, uploaded_img):
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"""Combine the given online research and GPT blog content"""
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try:
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config_path = Path(os.environ["ALWRITY_CONFIG"])
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with open(config_path, 'r', encoding='utf-8') as file:
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config = json.load(file)
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except Exception as err:
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logger.error(f"Error: Failed to read values from config: {err}")
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exit(1)
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blog_characteristics = config['Blog Content Characteristics']
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if not prompt:
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prompt = f"""
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As expert Creative Content writer, analyse the given image carefully.
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I want you to write a detailed {blog_characteristics['Blog Type']} blog post including 5 FAQs.
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Below are the guidelines to follow:
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1). You must respond in {blog_characteristics['Blog Language']} language.
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2). Tone and Brand Alignment: Adjust your tone, voice, personality for {blog_characteristics['Blog Tone']} audience.
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3). Make sure your response content length is of {blog_characteristics['Blog Length']} words.
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"""
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logger.info("Generating blog and FAQs from Google web search results.")
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try:
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#response = llm_text_gen(prompt)
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genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
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version = 'models/gemini-1.5-flash'
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model = genai.GenerativeModel(version)
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model_info = genai.get_model(version)
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print(f'{version} - input limit: {model_info.input_token_limit}, output limit: {model_info.output_token_limit}')
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response = model.generate_content([prompt, Image.open(uploaded_img)])
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return response.text
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except Exception as err:
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logger.error(f"Exit: Failed to get response from LLM: {err}")
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exit(1)
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