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