diff --git a/.gitignore b/.gitignore
index 74a3cefd..ff212253 100644
--- a/.gitignore
+++ b/.gitignore
@@ -52,6 +52,8 @@ web_research_report*
*.venv_*_*_*
*.venv_*_*_*_*
+website_analyzer.log
+
*venv
venv_new*
venv_*
diff --git a/lib/ai_seo_tools/meta_desc_generator.py b/lib/ai_seo_tools/meta_desc_generator.py
index c0ef0f37..69ad45ac 100644
--- a/lib/ai_seo_tools/meta_desc_generator.py
+++ b/lib/ai_seo_tools/meta_desc_generator.py
@@ -2,7 +2,9 @@ import os
import json
import streamlit as st
from tenacity import retry, stop_after_attempt, wait_random_exponential
-import google.generativeai as genai
+from loguru import logger
+import sys
+
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
@@ -103,5 +105,6 @@ def generate_blog_metadesc(keywords, tone, search_type, language):
try:
return llm_text_gen(prompt)
except Exception as err:
+ logger.error(f"Error generating meta description: {err}")
st.error(f"💥 Error: Failed to generate response from LLM: {err}")
return None
diff --git a/lib/ai_writers/ai_essay_writer.py b/lib/ai_writers/ai_essay_writer.py
index 06ecd08e..b13ef9ef 100644
--- a/lib/ai_writers/ai_essay_writer.py
+++ b/lib/ai_writers/ai_essay_writer.py
@@ -7,27 +7,29 @@
import os
from pathlib import Path
from dotenv import load_dotenv
-from google.api_core import retry
-import google.generativeai as genai
from pprint import pprint
+from loguru import logger
+import sys
+
+from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-def generate_with_retry(model, prompt):
+def generate_with_retry(prompt, system_prompt=None):
"""
- Generates content from the model with retry handling for errors.
+ Generates content using the llm_text_gen function with retry handling for errors.
Parameters:
- model (GenerativeModel): The generative model to use for content generation.
prompt (str): The prompt to generate content from.
+ system_prompt (str, optional): Custom system prompt to use instead of the default one.
Returns:
str: The generated content.
"""
try:
- # FIXME: Need a progress bar here.
- return model.generate_content(prompt, request_options={'retry':retry.Retry()})
+ # Use llm_text_gen instead of directly calling the model
+ return llm_text_gen(prompt, system_prompt)
except Exception as e:
- print(f"Error generating content: {e}")
+ logger.error(f"Error generating content: {e}")
return ""
@@ -36,11 +38,12 @@ def ai_essay_generator(essay_title, selected_essay_type, selected_education_leve
Write an Essay using prompt chaining and iterative generation.
Parameters:
- persona (str): The persona statement for the author.
- story_genre (str): The genre of the story.
- characters (str): The characters in the story.
+ essay_title (str): The title or topic of the essay.
+ selected_essay_type (str): The type of essay to write.
+ selected_education_level (str): The education level of the target audience.
+ selected_num_pages (int): The number of pages or words for the essay.
"""
- print(f"Starting to write Essay on {essay_title}..")
+ logger.info(f"Starting to write Essay on {essay_title}..")
try:
# Define persona and writing guidelines
guidelines = f'''\
@@ -127,59 +130,55 @@ def ai_essay_generator(essay_title, selected_essay_type, selected_education_leve
{guidelines}
'''
- # Configure generative AI
- load_dotenv(Path('../.env'))
- genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
- # Initialize the generative model
- model = genai.GenerativeModel('gemini-pro')
-
# Generate prompts
try:
- premise = generate_with_retry(model, premise_prompt).text
- print(f"The title of the Essay is: {premise}")
+ premise = generate_with_retry(premise_prompt)
+ logger.info(f"The title of the Essay is: {premise}")
except Exception as err:
- print(f"Essay title Generation Error: {err}")
+ logger.error(f"Essay title Generation Error: {err}")
return
- outline = generate_with_retry(model, outline_prompt.format(premise=premise)).text
- print(f"The Outline of the essay is: {outline}\n\n")
+ outline = generate_with_retry(outline_prompt.format(premise=premise))
+ logger.info(f"The Outline of the essay is: {outline}\n\n")
if not outline:
- print("Failed to generate Essay outline. Exiting...")
+ logger.error("Failed to generate Essay outline. Exiting...")
return
try:
- starting_draft = generate_with_retry(model,
- starting_prompt.format(premise=premise, outline=outline)).text
+ starting_draft = generate_with_retry(
+ starting_prompt.format(premise=premise, outline=outline))
pprint(starting_draft)
except Exception as err:
- print(f"Failed to Generate Essay draft: {err}")
+ logger.error(f"Failed to Generate Essay draft: {err}")
return
try:
draft = starting_draft
- continuation = generate_with_retry(model,
- continuation_prompt.format(premise=premise, outline=outline, story_text=draft)).text
+ continuation = generate_with_retry(
+ continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
pprint(continuation)
except Exception as err:
- print(f"Failed to write the initial draft: {err}")
+ logger.error(f"Failed to write the initial draft: {err}")
# Add the continuation to the initial draft, keep building the story until we see 'IAMDONE'
try:
draft += '\n\n' + continuation
except Exception as err:
- print(f"Failed as: {err} and {continuation}")
+ logger.error(f"Failed as: {err} and {continuation}")
while 'IAMDONE' not in continuation:
try:
- continuation = generate_with_retry(model,
- continuation_prompt.format(premise=premise, outline=outline, story_text=draft)).text
+ continuation = generate_with_retry(
+ continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
draft += '\n\n' + continuation
except Exception as err:
- print(f"Failed to continually write the Essay: {err}")
+ logger.error(f"Failed to continually write the Essay: {err}")
return
# Remove 'IAMDONE' and print the final story
final = draft.replace('IAMDONE', '').strip()
pprint(final)
+ return final
except Exception as e:
- print(f"Main Essay writing: An error occurred: {e}")
+ logger.error(f"Main Essay writing: An error occurred: {e}")
+ return ""
diff --git a/lib/ai_writers/ai_facebook_writer/facebook_ai_writer.py b/lib/ai_writers/ai_facebook_writer/facebook_ai_writer.py
index 683599b0..30ac1cf4 100644
--- a/lib/ai_writers/ai_facebook_writer/facebook_ai_writer.py
+++ b/lib/ai_writers/ai_facebook_writer/facebook_ai_writer.py
@@ -15,14 +15,14 @@ from pathlib import Path
from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
from .modules.post_generator import write_fb_post
from .modules.story_generator import write_fb_story
-from .modules.reel_generator import write_fb_reel
-from .modules.carousel_generator import write_fb_carousel
-from .modules.event_generator import write_fb_event
-from .modules.group_post_generator import write_fb_group_post
-from .modules.page_about_generator import write_fb_page_about
-from .modules.ad_copy_generator import write_fb_ad_copy
-from .modules.hashtag_generator import write_fb_hashtags
-from .modules.engagement_analyzer import analyze_fb_engagement
+#from .modules.reel_generator import write_fb_reel
+#from .modules.carousel_generator import write_fb_carousel
+#from .modules.event_generator import write_fb_event
+#from .modules.group_post_generator import write_fb_group_post
+#from .modules.page_about_generator import write_fb_page_about
+#from .modules.ad_copy_generator import write_fb_ad_copy
+#from .modules.hashtag_generator import write_fb_hashtags
+#from .modules.engagement_analyzer import analyze_fb_engagement
#from streamlit_quill import st_quill
diff --git a/lib/ai_writers/ai_facebook_writer/modules/post_generator.py b/lib/ai_writers/ai_facebook_writer/modules/post_generator.py
index 3358e188..a16e383a 100644
--- a/lib/ai_writers/ai_facebook_writer/modules/post_generator.py
+++ b/lib/ai_writers/ai_facebook_writer/modules/post_generator.py
@@ -6,8 +6,8 @@ and optimization options.
"""
import streamlit as st
-from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
-from ...gpt_providers.image_generation.main_image_generation import generate_image
+from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
+from ....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
def write_fb_post():
diff --git a/lib/ai_writers/ai_facebook_writer/modules/story_generator.py b/lib/ai_writers/ai_facebook_writer/modules/story_generator.py
index 492888ea..9ee8b56e 100644
--- a/lib/ai_writers/ai_facebook_writer/modules/story_generator.py
+++ b/lib/ai_writers/ai_facebook_writer/modules/story_generator.py
@@ -6,8 +6,8 @@ and customization options.
"""
import streamlit as st
-from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
-from ...gpt_providers.image_generation.main_image_generation import generate_image
+from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
+from ....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
def write_fb_story():
diff --git a/lib/ai_writers/ai_story_writer/ai_story_generator.py b/lib/ai_writers/ai_story_writer/ai_story_generator.py
index 34368b48..826c6c0d 100644
--- a/lib/ai_writers/ai_story_writer/ai_story_generator.py
+++ b/lib/ai_writers/ai_story_writer/ai_story_generator.py
@@ -6,27 +6,29 @@
import os
from pathlib import Path
-from google.api_core import retry
-import google.generativeai as genai
import streamlit as st
+from loguru import logger
+import sys
+
+from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
-def generate_with_retry(model, prompt):
+def generate_with_retry(prompt, system_prompt=None):
"""
- Generates content from the model with retry handling for errors.
+ Generates content using the llm_text_gen function with retry handling for errors.
Parameters:
- model (GenerativeModel): The generative model to use for content generation.
prompt (str): The prompt to generate content from.
+ system_prompt (str, optional): Custom system prompt to use instead of the default one.
Returns:
str: The generated content.
"""
try:
- # FIXME: Need a progress bar here.
- return model.generate_content(prompt, request_options={'retry':retry.Retry()})
+ # Use llm_text_gen instead of directly calling the model
+ return llm_text_gen(prompt, system_prompt)
except Exception as e:
- print(f"Error generating content: {e}")
+ logger.error(f"Error generating content: {e}")
return ""
@@ -38,8 +40,15 @@ def ai_story(persona, story_setting, character_input,
Parameters:
persona (str): The persona statement for the author.
- story_genre (str): The genre of the story.
- characters (str): The characters in the story.
+ story_setting (str): The setting of the story.
+ character_input (str): The characters in the story.
+ plot_elements (str): The plot elements of the story.
+ writing_style (str): The writing style of the story.
+ story_tone (str): The tone of the story.
+ narrative_pov (str): The narrative point of view.
+ audience_age_group (str): The target audience age group.
+ content_rating (str): The content rating of the story.
+ ending_preference (str): The preferred ending of the story.
"""
st.info(f"""
You have chosen to create a story set in **{story_setting}**.
@@ -170,20 +179,16 @@ def ai_story(persona, story_setting, character_input,
{guidelines}
'''
-
- genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
- # Initialize the generative model
- model = genai.GenerativeModel('gemini-1.5-flash')
# Generate prompts
try:
- premise = generate_with_retry(model, premise_prompt).text
+ premise = generate_with_retry(premise_prompt)
st.info(f"The premise of the story is: {premise}")
except Exception as err:
st.error(f"Premise Generation Error: {err}")
return
- outline = generate_with_retry(model, outline_prompt.format(premise=premise)).text
+ outline = generate_with_retry(outline_prompt.format(premise=premise))
with st.expander("Click to Checkout the outline, writing still in progress.."):
st.markdown(f"The Outline of the story is: {outline}\n\n")
@@ -193,16 +198,16 @@ def ai_story(persona, story_setting, character_input,
# Generate starting draft
try:
- starting_draft = generate_with_retry(model,
- starting_prompt.format(premise=premise, outline=outline)).text
+ starting_draft = generate_with_retry(
+ starting_prompt.format(premise=premise, outline=outline))
except Exception as err:
st.error(f"Failed to Generate Story draft: {err}")
return
try:
draft = starting_draft
- continuation = generate_with_retry(model,
- continuation_prompt.format(premise=premise, outline=outline, story_text=draft)).text
+ continuation = generate_with_retry(
+ continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
except Exception as err:
st.error(f"Failed to write the initial draft: {err}")
@@ -217,8 +222,8 @@ def ai_story(persona, story_setting, character_input,
while 'IAMDONE' not in continuation:
try:
status.update(label=f"Writing in progress... Current draft length: {len(draft)} characters")
- continuation = generate_with_retry(model,
- continuation_prompt.format(premise=premise, outline=outline, story_text=draft)).text
+ continuation = generate_with_retry(
+ continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
draft += '\n\n' + continuation
except Exception as err:
st.error(f"Failed to continually write the story: {err}")
@@ -230,3 +235,4 @@ def ai_story(persona, story_setting, character_input,
except Exception as e:
st.error(f"Main Story writing: An error occurred: {e}")
+ return ""
diff --git a/lib/ai_writers/image_ai_writer.py b/lib/ai_writers/image_ai_writer.py
index 5ec53509..64a4ae6f 100644
--- a/lib/ai_writers/image_ai_writer.py
+++ b/lib/ai_writers/image_ai_writer.py
@@ -2,7 +2,6 @@ import sys
import os
from textwrap import dedent
-from PIL import Image
import json
import asyncio
from pathlib import Path
@@ -23,8 +22,7 @@ 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
+from ..gpt_providers.image_to_text_gen.gemini_image_describe import describe_image, analyze_image_with_prompt
def blog_from_image(prompt, uploaded_img):
@@ -97,17 +95,15 @@ def write_blog_from_image(prompt, uploaded_img):
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.")
+ logger.info("Generating blog and FAQs from image analysis.")
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
+ # Use the gemini_image_describe function to analyze the image with the custom prompt
+ response = analyze_image_with_prompt(uploaded_img, prompt)
+ if not response:
+ logger.error("Failed to get response from image analysis")
+ return "Failed to generate content from image."
+ return response
except Exception as err:
- logger.error(f"Exit: Failed to get response from LLM: {err}")
+ logger.error(f"Exit: Failed to get response from image analysis: {err}")
exit(1)
diff --git a/lib/ai_writers/long_form_ai_writer.py b/lib/ai_writers/long_form_ai_writer.py
index 2f8c919e..d3401236 100644
--- a/lib/ai_writers/long_form_ai_writer.py
+++ b/lib/ai_writers/long_form_ai_writer.py
@@ -15,8 +15,6 @@ from dotenv import load_dotenv
from configparser import ConfigParser
import streamlit as st
-from google.api_core import retry
-import google.generativeai as genai
from pprint import pprint
from textwrap import dedent
@@ -32,22 +30,23 @@ from ..ai_web_researcher.gpt_online_researcher import do_metaphor_ai_research
from ..ai_web_researcher.gpt_online_researcher import do_google_serp_search, do_tavily_ai_search
from ..blog_metadata.get_blog_metadata import get_blog_metadata_longform
from ..blog_postprocessing.save_blog_to_file import save_blog_to_file
+from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-def generate_with_retry(model, prompt):
+def generate_with_retry(prompt, system_prompt=None):
"""
Generates content from the model with retry handling for errors.
Parameters:
- model (GenerativeModel): The generative model to use for content generation.
prompt (str): The prompt to generate content from.
+ system_prompt (str, optional): Custom system prompt to use instead of the default one.
Returns:
str: The generated content.
"""
try:
# FIXME: Need a progress bar here.
- return model.generate_content(prompt, request_options={'retry':retry.Retry()})
+ return llm_text_gen(prompt, system_prompt)
except Exception as e:
logger.error(f"Error generating content: {e}")
st.error(f"Error generating content: {e}")
@@ -57,7 +56,12 @@ def generate_with_retry(model, prompt):
def long_form_generator(content_keywords):
"""
Write long form content using prompt chaining and iterative generation.
+
Parameters:
+ content_keywords (str): The main keywords or topic for the long-form content.
+
+ Returns:
+ str: The generated long-form content.
"""
with st.status("Start Writing Long Form Article, Hold my Beer..", expanded=True) as status:
# Read the main_config to define tone, character, personality of the content to be generated.
@@ -122,39 +126,27 @@ def long_form_generator(content_keywords):
writing_guidelines=writing_guidelines
)
- # Configure generative AI
- load_dotenv(Path('../.env'))
- generation_config = {
- "temperature": 0.7,
- "top_p": 1,
- "max_output_tokens": 8096,
- }
-
- genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
- # Initialize the generative model
- model_flash = genai.GenerativeModel('gemini-1.5-flash', generation_config=generation_config)
- model_pro = genai.GenerativeModel('gemini-pro', generation_config=generation_config)
-
# Do SERP web research for given keywords to generate title and outline.
web_research_result, g_titles = do_google_serp_search(content_keywords)
# Generate prompts
try:
- content_title = generate_with_retry(model_pro, content_title.format(web_research_result=web_research_result)).text
+ content_title = generate_with_retry(content_title.format(web_research_result=web_research_result))
logger.info(f"The title of the content is: {content_title}")
status.update(label=f"The title of the content is: {content_title}")
except Exception as err:
logger.error(f"Content title Generation Error: {err}")
- return
+ return False
try:
- content_outline = generate_with_retry(model_flash, content_outline.format(
+ content_outline = generate_with_retry(content_outline.format(
content_title=content_title,
- web_research_result=web_research_result)).text
+ web_research_result=web_research_result))
logger.info(f"The content Outline is: {content_outline}\n\n")
status.update(label=f"Completed with Content Outline.")
except Exception as err:
logger.error(f"Failed to generate content outline: {err}")
+ return False
try:
status.update(label=f"Do web research with Tavily to provide context for content creation.")
@@ -170,36 +162,38 @@ def long_form_generator(content_keywords):
except Exception as err:
logger.error(f"Failed to do Tavily AI search: {err}")
st.error(f"Failed to do Tavily AI search: {err}")
- return
+ return False
try:
- starting_draft = generate_with_retry(model_pro, starting_prompt.format(
+ starting_draft = generate_with_retry(starting_prompt.format(
content_title=content_title,
content_outline=content_outline,
web_research_result=web_research_result,
- writing_guidelines=writing_guidelines)).text
+ writing_guidelines=writing_guidelines))
except Exception as err:
st.error(f"Failed to Generate Starting draft: {err}")
logger.error(f"Failed to Generate Starting draft: {err}")
- return
+ return False
try:
logger.info(f"Starting to write on the outline introduction.")
draft = starting_draft
- continuation = generate_with_retry(model_pro, continuation_prompt.format(
+ continuation = generate_with_retry(continuation_prompt.format(
content_title=content_title,
content_outline=content_outline,
content_text=draft,
web_research_result=web_research_result,
- writing_guidelines=writing_guidelines)).text
+ writing_guidelines=writing_guidelines))
except Exception as err:
logger.error(f"Failed to write the initial draft: {err}")
+ return False
# Add the continuation to the initial draft, keep building the story until we see 'IAMDONE'
try:
draft += '\n\n' + continuation
except Exception as err:
logger.error(f"Failed as: {err} and {continuation}")
+ return False
logger.info(f"Writing in progress... Current draft length: {len(draft)} characters")
status.update(label=f"Writing in progress... Current draft length: {len(draft)} characters")
@@ -211,7 +205,7 @@ def long_form_generator(content_keywords):
Content Outline:\n
'{content_outline}'
"""
- search_words = generate_with_retry(model_flash, search_terms).text
+ search_words = generate_with_retry(search_terms)
status.update(label=f"Search terms from written draft: {search_words}")
while 'IAMDONE' not in continuation:
@@ -230,12 +224,12 @@ def long_form_generator(content_keywords):
# web_research_result = table_data
try:
- continuation = generate_with_retry(model_pro, continuation_prompt.format(
+ continuation = generate_with_retry(continuation_prompt.format(
content_title=content_title,
content_outline=content_outline,
content_text=draft,
web_research_result=web_research_result,
- writing_guidelines=writing_guidelines)).text
+ writing_guidelines=writing_guidelines))
draft += '\n\n' + continuation
logger.info(f"Writing in progress... Current draft length: {len(draft)} characters")
@@ -245,7 +239,7 @@ def long_form_generator(content_keywords):
except Exception as err:
st.error(f"Failed to continually write long-form content: {err}")
logger.error(f"Failed to continually write the Essay: {err}")
- return
+ return False
# Remove 'IAMDONE' and print the final story
final = draft.replace('IAMDONE', '').strip()
@@ -267,3 +261,26 @@ def long_form_generator(content_keywords):
logger.info(f"\n\n ################ Finished writing Blog for : {content_keywords} #################### \n")
with st.expander("**Click to View the final content draft:**"):
st.markdown(f"\n{final}\n\n")
+
+ return final
+
+
+def generate_long_form_content(content_keywords):
+ """
+ Main function to generate long-form content based on the provided keywords.
+
+ Parameters:
+ content_keywords (str): The main keywords or topic for the long-form content.
+
+ Returns:
+ str: The generated long-form content.
+ """
+ return long_form_generator(content_keywords)
+
+
+# Example usage
+if __name__ == "__main__":
+ # Example usage of the function
+ content_keywords = "artificial intelligence in healthcare"
+ generated_content = generate_long_form_content(content_keywords)
+ print(f"Generated content: {generated_content[:100]}...")
diff --git a/lib/blog_metadata/get_blog_metadata.py b/lib/blog_metadata/get_blog_metadata.py
index dd34585b..1fe59044 100644
--- a/lib/blog_metadata/get_blog_metadata.py
+++ b/lib/blog_metadata/get_blog_metadata.py
@@ -12,8 +12,6 @@ logger.add(sys.stdout,
colorize=True,
format="{level}|{file}:{line}:{function}| {message}"
)
-import google.generativeai as genai
-from google.generativeai import caching
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
@@ -142,37 +140,38 @@ def get_blog_metadata_longform(longform_content):
file.write(longform_content)
print(f"String saved successfully to: {filepath}")
- genai.configure(api_key=os.environ['GEMINI_API_KEY'])
- file_path = genai.upload_file(path=filepath)
+ #genai.configure(api_key=os.environ['GEMINI_API_KEY'])
+ #file_path = genai.upload_file(path=filepath)
# Wait for the file to finish processing
- while file_path.state.name == 'PROCESSING':
- print('Waiting for video to be processed.')
- time.sleep(2)
- file_path = genai.get_file(video_file.name)
+ #while file_path.state.name == 'PROCESSING':
+ # print('Waiting for video to be processed.')
+ # time.sleep(2)
+ # file_path = genai.get_file(video_file.name)
- print(f'Video processing complete: {file_path.uri}')
+ #print(f'Video processing complete: {file_path.uri}')
# Create a cache with a 5 minute TTL
- cache = caching.CachedContent.create(
- model='models/gemini-1.5-flash-001',
- display_name='Alwrity Longform content', # used to identify the cache
- system_instruction=(
- 'You are an expert file analyzer , and your job is to answer '
- 'the user\'s query based on the file you have access to.'
- ),
- contents=[file_path],
- ttl=datetime.timedelta(minutes=15),
- )
+ #cache = caching.CachedContent.create(
+ # model='models/gemini-1.5-flash-001',
+ # display_name='Alwrity Longform content', # used to identify the cache
+ # system_instruction=(
+ # 'You are an expert file analyzer , and your job is to answer '
+ # 'the user\'s query based on the file you have access to.'
+ # ),
+ # contents=[file_path],
+ # ttl=datetime.timedelta(minutes=15),
+ #)
# Construct a GenerativeModel which uses the created cache.
- model = genai.GenerativeModel.from_cached_content(cached_content=cache)
+ #model = genai.GenerativeModel.from_cached_content(cached_content=cache)
# Query the model
- response = model.generate_content([(
- 'SUmmarize the given file '
- 'in 10 lines '
- 'list main points')])
+ #response = model.generate_content([(
+ # 'SUmmarize the given file '
+ # 'in 10 lines '
+ # 'list main points')])
#print(response.usage_metadata)
- return(response.text)
+ #return(response.text)
+ return("TBD: Not implemented")
diff --git a/lib/gpt_providers/audio_to_text_generation/gemini_audio_text.py b/lib/gpt_providers/audio_to_text_generation/gemini_audio_text.py
index 5625dcea..1e343fd9 100644
--- a/lib/gpt_providers/audio_to_text_generation/gemini_audio_text.py
+++ b/lib/gpt_providers/audio_to_text_generation/gemini_audio_text.py
@@ -1,8 +1,75 @@
+"""
+Gemini Audio Text Generation Module
+
+This module provides a comprehensive interface for working with audio files using Google's Gemini API.
+It supports various audio processing capabilities including transcription, summarization, and analysis.
+
+Key Features:
+------------
+1. Audio Transcription: Convert speech in audio files to text
+2. Audio Summarization: Generate concise summaries of audio content
+3. Segment Analysis: Analyze specific time segments of audio files
+4. Timestamped Transcription: Generate transcriptions with timestamps
+5. Token Counting: Count tokens in audio files
+6. Format Support: Information about supported audio formats
+
+Supported Audio Formats:
+----------------------
+- WAV (audio/wav)
+- MP3 (audio/mp3)
+- AIFF (audio/aiff)
+- AAC (audio/aac)
+- OGG Vorbis (audio/ogg)
+- FLAC (audio/flac)
+
+Technical Details:
+----------------
+- Each second of audio is represented as 32 tokens
+- Maximum supported length of audio data in a single prompt is 9.5 hours
+- Audio files are downsampled to 16 Kbps data resolution
+- Multi-channel audio is combined into a single channel
+
+Usage:
+------
+```python
+from lib.gpt_providers.audio_to_text_generation.gemini_audio_text import transcribe_audio, summarize_audio
+
+# Basic transcription
+transcript = transcribe_audio("path/to/audio.mp3")
+print(transcript)
+
+# Summarization
+summary = summarize_audio("path/to/audio.mp3")
+print(summary)
+
+# Analyze specific segment
+segment_analysis = analyze_audio_segment("path/to/audio.mp3", "02:30", "03:29")
+print(segment_analysis)
+```
+
+Requirements:
+------------
+- GEMINI_API_KEY environment variable must be set
+- google-generativeai Python package
+- python-dotenv for environment variable management
+- loguru for logging
+
+Dependencies:
+------------
+- google.genai
+- dotenv
+- loguru
+- os, sys, base64, typing
+"""
+
import os
import sys
-
-import google.generativeai as genai
+import base64
+from typing import Optional, Dict, Any, List, Union
from dotenv import load_dotenv
+from google import genai
+from google.genai import types
+
from loguru import logger
logger.remove()
@@ -34,12 +101,13 @@ def configure_google_api():
logger.info("Google Gemini API configured successfully.")
-def transcribe_audio(audio_file_path):
+def transcribe_audio(audio_file_path: str, prompt: str = "Transcribe the following audio:") -> Optional[str]:
"""
- Transcribes audio using Google's Gemini Pro model.
+ Transcribes audio using Google's Gemini model.
Args:
audio_file_path (str): The path to the audio file to be transcribed.
+ prompt (str, optional): The prompt to guide the transcription. Defaults to "Transcribe the following audio:".
Returns:
str: The transcribed text from the audio.
@@ -61,7 +129,7 @@ def transcribe_audio(audio_file_path):
logger.error(error_message)
raise FileNotFoundError(error_message)
- # Initialize a Gemini model appropriate for your use case.
+ # Initialize a Gemini model appropriate for audio understanding
model = genai.GenerativeModel(model_name="gemini-1.5-flash")
# Upload the audio file
@@ -79,7 +147,7 @@ def transcribe_audio(audio_file_path):
# Generate the transcription
try:
response = model.generate_content([
- "Transcribe the following audio:",
+ prompt,
audio_file
])
@@ -99,3 +167,143 @@ def transcribe_audio(audio_file_path):
except Exception as e:
logger.error(f"An unexpected error occurred: {e}")
return None
+
+
+def summarize_audio(audio_file_path: str) -> Optional[str]:
+ """
+ Summarizes the content of an audio file using Google's Gemini model.
+
+ Args:
+ audio_file_path (str): The path to the audio file to be summarized.
+
+ Returns:
+ str: A summary of the audio content.
+ Returns None if summarization fails.
+ """
+ return transcribe_audio(audio_file_path, prompt="Please summarize the audio content:")
+
+
+def analyze_audio_segment(audio_file_path: str, start_time: str, end_time: str) -> Optional[str]:
+ """
+ Analyzes a specific segment of an audio file using timestamps.
+
+ Args:
+ audio_file_path (str): The path to the audio file.
+ start_time (str): Start time in MM:SS format.
+ end_time (str): End time in MM:SS format.
+
+ Returns:
+ str: Analysis of the specified audio segment.
+ Returns None if analysis fails.
+ """
+ prompt = f"Analyze the audio content from {start_time} to {end_time}."
+ return transcribe_audio(audio_file_path, prompt=prompt)
+
+
+def transcribe_with_timestamps(audio_file_path: str) -> Optional[str]:
+ """
+ Transcribes audio with timestamps for each segment.
+
+ Args:
+ audio_file_path (str): The path to the audio file.
+
+ Returns:
+ str: Transcription with timestamps.
+ Returns None if transcription fails.
+ """
+ return transcribe_audio(audio_file_path, prompt="Transcribe the audio with timestamps for each segment:")
+
+
+def count_tokens(audio_file_path: str) -> Optional[int]:
+ """
+ Counts the number of tokens in an audio file.
+
+ Args:
+ audio_file_path (str): The path to the audio file.
+
+ Returns:
+ int: Number of tokens in the audio file.
+ Returns None if counting fails.
+ """
+ try:
+ # Load environment variables and configure the Google API
+ load_environment()
+ configure_google_api()
+
+ logger.info(f"Attempting to count tokens in audio file: {audio_file_path}")
+
+ # Check if file exists
+ if not os.path.exists(audio_file_path):
+ error_message = f"FileNotFoundError: The audio file at {audio_file_path} does not exist."
+ logger.error(error_message)
+ raise FileNotFoundError(error_message)
+
+ # Initialize a Gemini model
+ model = genai.GenerativeModel(model_name="gemini-1.5-flash")
+
+ # Upload the audio file
+ try:
+ audio_file = genai.upload_file(audio_file_path)
+ logger.info(f"Audio file uploaded successfully: {audio_file=}")
+ except Exception as e:
+ logger.error(f"Error uploading audio file: {e}")
+ return None
+
+ # Count tokens
+ try:
+ response = model.count_tokens([audio_file])
+ token_count = response.total_tokens
+ logger.info(f"Token count: {token_count}")
+ return token_count
+ except Exception as e:
+ logger.error(f"Error counting tokens: {e}")
+ return None
+
+ except Exception as e:
+ logger.error(f"An unexpected error occurred: {e}")
+ return None
+
+
+def get_supported_formats() -> List[str]:
+ """
+ Returns a list of supported audio formats.
+
+ Returns:
+ List[str]: List of supported MIME types.
+ """
+ return [
+ "audio/wav",
+ "audio/mp3",
+ "audio/aiff",
+ "audio/aac",
+ "audio/ogg",
+ "audio/flac"
+ ]
+
+
+# Example usage
+if __name__ == "__main__":
+ # Example 1: Basic transcription
+ audio_path = "path/to/your/audio.mp3"
+ transcript = transcribe_audio(audio_path)
+ print(f"Transcript: {transcript}")
+
+ # Example 2: Summarization
+ summary = summarize_audio(audio_path)
+ print(f"Summary: {summary}")
+
+ # Example 3: Analyze specific segment
+ segment_analysis = analyze_audio_segment(audio_path, "02:30", "03:29")
+ print(f"Segment Analysis: {segment_analysis}")
+
+ # Example 4: Transcription with timestamps
+ timestamped_transcript = transcribe_with_timestamps(audio_path)
+ print(f"Timestamped Transcript: {timestamped_transcript}")
+
+ # Example 5: Count tokens
+ token_count = count_tokens(audio_path)
+ print(f"Token Count: {token_count}")
+
+ # Example 6: Get supported formats
+ formats = get_supported_formats()
+ print(f"Supported Formats: {formats}")
diff --git a/lib/gpt_providers/image_to_text_gen/gemini_image_describe.py b/lib/gpt_providers/image_to_text_gen/gemini_image_describe.py
new file mode 100644
index 00000000..00bd1c3a
--- /dev/null
+++ b/lib/gpt_providers/image_to_text_gen/gemini_image_describe.py
@@ -0,0 +1,116 @@
+"""
+Gemini Image Description Module
+
+This module provides functionality to generate text descriptions of images using Google's Gemini API.
+"""
+
+import os
+import sys
+from typing import Optional, Union, List
+
+from google import genai
+from PIL import Image
+from dotenv import load_dotenv
+
+from loguru import logger
+logger.remove()
+logger.add(sys.stdout,
+ colorize=True,
+ format="{level}|{file}:{line}:{function}| {message}"
+ )
+
+
+def describe_image(image_path: str, prompt: str = "Describe this image in detail:") -> Optional[str]:
+ """
+ Generate a text description of an image using Google's Gemini API.
+
+ Parameters:
+ image_path (str): Path to the image file.
+ prompt (str, optional): Custom prompt to guide the image description.
+ Defaults to "Describe this image in detail:".
+
+ Returns:
+ Optional[str]: The generated description of the image, or None if an error occurs.
+
+ Raises:
+ FileNotFoundError: If the image file does not exist.
+ ValueError: If the API key is not set.
+ """
+ try:
+ # Load environment variables
+ load_dotenv()
+
+ # Check if API key is set
+ api_key = os.getenv('GEMINI_API_KEY')
+ if not api_key:
+ error_message = "GEMINI_API_KEY environment variable is not set"
+ logger.error(error_message)
+ raise ValueError(error_message)
+
+ # Check if image file exists
+ if not os.path.exists(image_path):
+ error_message = f"Image file not found: {image_path}"
+ logger.error(error_message)
+ raise FileNotFoundError(error_message)
+
+ # Initialize the Gemini client
+ client = genai.Client(api_key=api_key)
+
+ # Open and process the image
+ try:
+ image = Image.open(image_path)
+ logger.info(f"Successfully opened image: {image_path}")
+ except Exception as e:
+ error_message = f"Failed to open image: {e}"
+ logger.error(error_message)
+ return None
+
+ # Generate content description
+ try:
+ response = client.models.generate_content(
+ model='gemini-2.0-flash',
+ contents=[
+ prompt,
+ image
+ ]
+ )
+
+ # Extract and return the text
+ description = response.text
+ logger.info(f"Successfully generated description for image: {image_path}")
+ return description
+
+ except Exception as e:
+ error_message = f"Failed to generate content: {e}"
+ logger.error(error_message)
+ return None
+
+ except Exception as e:
+ error_message = f"An unexpected error occurred: {e}"
+ logger.error(error_message)
+ return None
+
+
+def analyze_image_with_prompt(image_path: str, prompt: str) -> Optional[str]:
+ """
+ Analyze an image with a custom prompt using Google's Gemini API.
+
+ Parameters:
+ image_path (str): Path to the image file.
+ prompt (str): Custom prompt for analyzing the image.
+
+ Returns:
+ Optional[str]: The generated analysis of the image, or None if an error occurs.
+ """
+ return describe_image(image_path, prompt)
+
+
+# Example usage
+if __name__ == "__main__":
+ # Example usage of the function
+ image_path = "path/to/your/image.jpg"
+ description = describe_image(image_path)
+ if description:
+ print(f"Image description: {description}")
+ else:
+ print("Failed to generate image description")
\ No newline at end of file
diff --git a/lib/gpt_providers/image_to_text_gen/openai_vision_img_details.py b/lib/gpt_providers/image_to_text_gen/openai_vision_img_details.py
deleted file mode 100644
index 384ea0db..00000000
--- a/lib/gpt_providers/image_to_text_gen/openai_vision_img_details.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import requests
-import re
-import base64
-import os
-import sys
-from tenacity import (
- retry,
- stop_after_attempt,
- wait_random_exponential,
-) # for exponential backoff
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def analyze_and_extract_details_from_image(image_path):
- """
- Analyzes an image using OpenAI's Vision API to extract Alt Text, Description, Title, and Caption.
-
- This function encodes an image to a base64 string and sends a request to the OpenAI API.
- It interprets the contents of the image, returning a textual description.
-
- Args:
- image_path (str): Path to the image file.
-
- Returns:
- dict: A dictionary with extracted details including Alt Text, Description, Title, and Caption.
- None: If an error occurs during processing.
-
- Raises:
- SystemExit: If a critical error occurs that prevents the function from executing successfully.
- """
- try:
- logger.info("Starting image analysis using OpenAI's Vision API.")
-
- def encode_image(path):
- """ Encodes an image to a base64 string. """
- with open(path, "rb", encoding="utf-8") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
- base64_image = encode_image(image_path)
- logger.info("Image encoded to base64 successfully.")
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
- }
-
- payload = {
- "model": "gpt-4-vision-preview",
- "messages": [
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": "Analyze the given image and suggest the following: Alternative text(Alt Text), description, title, caption."
- },
- {
- "type": "image_url",
- "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
- }
- ]
- }
- ],
- "max_tokens": 300
- }
-
- response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
- response.raise_for_status()
-
- assistant_message = response.json()['choices'][0]['message']['content']
- logger.info("Received response from OpenAI API.")
-
- # Extracting details using regular expressions
- alt_text_match = re.search(r'Alt Text: "(.*?)"', assistant_message)
- description_match = re.search(r'Description: (.*?)\n\n', assistant_message)
- title_match = re.search(r'Title: "(.*?)"', assistant_message)
- caption_match = re.search(r'Caption: "(.*?)"', assistant_message)
-
- image_details = {
- 'alt_text': alt_text_match.group(1) if alt_text_match else "N/A",
- 'description': description_match.group(1) if description_match else "N/A",
- 'title': title_match.group(1) if title_match else "N/A",
- 'caption': caption_match.group(1) if caption_match else "N/A"
- }
-
- logger.info("Image analysis completed successfully.")
- return image_details
-
- except requests.RequestException as e:
- logger.error(f"GPT-Vision API communication failure. Error: {e}")
- sys.exit(f"Exiting due to GPT-Vision API communication failure: {e}")
-
- except Exception as e:
- logger.error(f"Unexpected error occurred during image analysis: {e}")
- sys.exit(f"Exiting due to an unexpected error: {e}")
diff --git a/lib/utils/alwrity_utils.py b/lib/utils/alwrity_utils.py
index 5cb5d6a4..540a555e 100644
--- a/lib/utils/alwrity_utils.py
+++ b/lib/utils/alwrity_utils.py
@@ -1,7 +1,12 @@
import re
+import os
+import PyPDF2
+import tiktoken
+import openai
import streamlit as st
import tempfile
from loguru import logger
+
from lib.ai_web_researcher.gpt_online_researcher import gpt_web_researcher
from lib.ai_writers.keywords_to_blog_streamlit import write_blog_from_keywords
from lib.ai_writers.speech_to_blog.main_audio_to_blog import generate_audio_blog
@@ -9,7 +14,7 @@ from lib.ai_writers.long_form_ai_writer import long_form_generator
from lib.ai_writers.ai_news_article_writer import ai_news_generation
#from lib.ai_writers.ai_agents_crew_writer import ai_agents_writers
from lib.ai_writers.ai_financial_writer import write_basic_ta_report
-from lib.ai_writers.ai_facebook_writer.facebook_ai_writer import facebook_post_writer
+from lib.ai_writers.ai_facebook_writer.facebook_ai_writer import facebook_main_menu
from lib.ai_writers.linkedin_ai_writer import linked_post_writer
from lib.ai_writers.twitter_ai_writer import tweet_writer
from lib.ai_writers.insta_ai_writer import insta_writer
@@ -17,10 +22,6 @@ from lib.ai_writers.youtube_writers.youtube_ai_writer import youtube_main_menu
from lib.ai_writers.web_url_ai_writer import blog_from_url
from lib.ai_writers.image_ai_writer import blog_from_image
from lib.ai_writers.ai_essay_writer import ai_essay_generator
-import os
-import PyPDF2
-import tiktoken
-import openai
from lib.gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
from lib.utils.voice_processing import record_voice
#from lib.content_planning_calender.content_planning_agents_alwrity_crew import ai_agents_content_planner
@@ -465,7 +466,7 @@ def ai_social_writer():
# Selectbox for choosing a platform
selected_platform = st.radio("Choose a Social Media Platform:", social_media_options, format_func=lambda x: x[1])
if "facebook" in selected_platform:
- facebook_post_writer()
+ facebook_main_menu()
elif "linkedin" in selected_platform:
linked_post_writer()
elif "twitter" in selected_platform:
diff --git a/lib/utils/api_key_manager/components/ai_providers.py b/lib/utils/api_key_manager/components/ai_providers.py
index c55aae6c..441ef2f5 100644
--- a/lib/utils/api_key_manager/components/ai_providers.py
+++ b/lib/utils/api_key_manager/components/ai_providers.py
@@ -19,6 +19,17 @@ def render_ai_providers(api_key_manager: APIKeyManager) -> Dict[str, Any]:
"""Render the AI providers setup step."""
logger.info("[render_ai_providers] Starting AI providers setup")
try:
+ # Initialize wizard state if not already initialized
+ if 'wizard_state' not in st.session_state:
+ st.session_state.wizard_state = {
+ 'current_step': 1,
+ 'total_steps': 6,
+ 'progress': 0,
+ 'completed_steps': set(),
+ 'last_updated': datetime.now()
+ }
+ logger.info("[render_ai_providers] Initialized wizard state")
+
# Store API key manager in session state for update_progress
st.session_state['api_key_manager'] = api_key_manager
@@ -209,6 +220,15 @@ def render_ai_providers(api_key_manager: APIKeyManager) -> Dict[str, Any]:
'google': google_key if validate_api_key(google_key) else None
}
+ # Save API keys to .env file
+ if validate_api_key(openai_key):
+ api_key_manager.save_api_key("openai", openai_key)
+ logger.info("[render_ai_providers] OpenAI API key saved to .env file")
+
+ if validate_api_key(google_key):
+ api_key_manager.save_api_key("gemini", google_key)
+ logger.info("[render_ai_providers] Google Gemini API key saved to .env file")
+
# Update progress and move to next step
st.session_state['current_step'] = 2 # Set the next step explicitly
update_progress()
diff --git a/lib/utils/api_key_manager/components/final_setup.py b/lib/utils/api_key_manager/components/final_setup.py
index 5f9eb06c..5dc2654a 100644
--- a/lib/utils/api_key_manager/components/final_setup.py
+++ b/lib/utils/api_key_manager/components/final_setup.py
@@ -91,18 +91,15 @@ def render_final_setup(api_key_manager: APIKeyManager) -> Dict[str, Any]:
logger.info("[render_final_setup] User clicked complete setup")
try:
# Verify all required API keys are present and valid
- is_valid, missing_keys, impact_messages = check_all_api_keys(api_key_manager)
+ is_valid = check_all_api_keys(api_key_manager)
if not is_valid:
st.error("⚠️ Some required API keys are missing")
st.markdown("### Missing API Keys and Impact")
- # Display impact messages in a structured way
- for message in impact_messages:
- if message.startswith("⚠️"):
- st.error(message)
- else:
- st.warning(message)
+ # Display impact messages
+ st.warning("⚠️ Missing AI Provider: At least one AI provider (OpenAI, Google Gemini, Anthropic Claude, or Mistral) is required.")
+ st.warning("⚠️ Missing Research Provider: At least one research provider (SerpAPI, Tavily, Metaphor, or Firecrawl) is required.")
st.markdown("""
diff --git a/lib/utils/api_key_manager/manager.py b/lib/utils/api_key_manager/manager.py
index 213c4d27..2897c1be 100644
--- a/lib/utils/api_key_manager/manager.py
+++ b/lib/utils/api_key_manager/manager.py
@@ -133,16 +133,74 @@ class APIKeyManager:
except Exception as e:
logger.error(f"[APIKeyManager.load_api_keys] Error loading API keys: {str(e)}")
- def save_api_key(self, provider: str, key: str):
- """Save an API key."""
- logger.info(f"[APIKeyManager.save_api_key] Saving API key for provider: {provider}")
+ def save_api_key(self, provider: str, api_key: str) -> bool:
+ """
+ Save an API key for a provider.
+
+ Args:
+ provider: The provider name (e.g., 'openai', 'gemini')
+ api_key: The API key value
+
+ Returns:
+ bool: True if successful, False otherwise
+ """
try:
- self.api_keys[provider] = key
- # Save to environment variable
- os.environ[f"{provider.upper()}_API_KEY"] = key
- logger.info(f"[APIKeyManager.save_api_key] Successfully saved API key for {provider}")
+ logger.info(f"[APIKeyManager] Saving API key for {provider}")
+
+ # Map provider to environment variable name
+ env_var_map = {
+ 'openai': 'OPENAI_API_KEY',
+ 'gemini': 'GEMINI_API_KEY',
+ 'mistral': 'MISTRAL_API_KEY',
+ 'anthropic': 'ANTHROPIC_API_KEY',
+ 'serpapi': 'SERPAPI_API_KEY',
+ 'tavily': 'TAVILY_API_KEY',
+ 'metaphor': 'METAPHOR_API_KEY',
+ 'firecrawl': 'FIRECRAWL_API_KEY'
+ }
+
+ env_var = env_var_map.get(provider)
+ if not env_var:
+ logger.error(f"[APIKeyManager] Unknown provider: {provider}")
+ return False
+
+ # Update the in-memory dictionary
+ self.api_keys[provider] = api_key
+
+ # Update environment variable
+ os.environ[env_var] = api_key
+
+ # Read existing .env file content
+ env_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), '.env')
+ try:
+ with open(env_path, 'r', encoding='utf-8') as f:
+ lines = f.readlines()
+ except FileNotFoundError:
+ lines = []
+
+ # Update or add the API key
+ key_found = False
+ updated_lines = []
+ for line in lines:
+ if line.startswith(f"{env_var}="):
+ updated_lines.append(f"{env_var}={api_key}\n")
+ key_found = True
+ else:
+ updated_lines.append(line)
+
+ if not key_found:
+ updated_lines.append(f"{env_var}={api_key}\n")
+
+ # Write back to .env file
+ with open(env_path, 'w', encoding='utf-8') as f:
+ f.writelines(updated_lines)
+
+ logger.info(f"[APIKeyManager] Successfully saved API key for {provider}")
+ return True
+
except Exception as e:
- logger.error(f"[APIKeyManager.save_api_key] Error saving API key: {str(e)}")
+ logger.error(f"[APIKeyManager] Error saving API key for {provider}: {str(e)}")
+ return False
def get_api_key(self, provider: str) -> Optional[str]:
"""Get an API key."""
diff --git a/lib/utils/ui_setup.py b/lib/utils/ui_setup.py
index 768d7f15..f3168579 100644
--- a/lib/utils/ui_setup.py
+++ b/lib/utils/ui_setup.py
@@ -6,7 +6,7 @@ from lib.utils.alwrity_utils import ai_social_writer
from lib.utils.seo_tools import ai_seo_tools
from lib.utils.settings_page import render_settings_page
# Import social media writer functions
-from lib.ai_writers.ai_facebook_writer.facebook_ai_writer import facebook_post_writer
+from lib.ai_writers.ai_facebook_writer.facebook_ai_writer import facebook_main_menu
from lib.ai_writers.linkedin_ai_writer import linked_post_writer
from lib.ai_writers.twitter_ai_writer import tweet_writer
from lib.ai_writers.insta_ai_writer import insta_writer