kunthawat/ALwrity
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Gemini AI common code and utils

Browse Source
This commit is contained in:
ajaysi
2025-04-11 17:47:55 +05:30
parent b556edb989
commit e41be5789a
18 changed files with 590 additions and 262 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
lib/blog_metadata/get_blog_metadata.py
View File

@@ -12,8 +12,6 @@ logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {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")