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import logging
import requests
from global_config import GlobalConfig
HF_API_URL = f"https://api-inference.huggingface.co/models/{GlobalConfig.HF_LLM_MODEL_NAME}"
HF_API_HEADERS = {"Authorization": f"Bearer {GlobalConfig.HUGGINGFACEHUB_API_TOKEN}"}
logging.basicConfig(
level=GlobalConfig.LOG_LEVEL,
format='%(asctime)s - %(message)s',
)
# llm = None
def hf_api_query(payload: dict):
"""
Invoke HF inference end-point API.
:param payload: The prompt for the LLM and related parameters
:return: The output from the LLM
"""
try:
response = requests.post(HF_API_URL, headers=HF_API_HEADERS, json=payload, timeout=15)
result = response.json()
except requests.exceptions.Timeout as te:
logging.error('*** Error: hf_api_query timeout! %s', str(te))
result = {}
return result
def generate_slides_content(topic: str) -> str:
"""
Generate the outline/contents of slides for a presentation on a given topic.
:param topic: Topic on which slides are to be generated
:return: The content in JSON format
"""
with open(GlobalConfig.SLIDES_TEMPLATE_FILE, 'r', encoding='utf-8') as in_file:
template_txt = in_file.read().strip()
template_txt = template_txt.replace('<REPLACE_PLACEHOLDER>', topic)
output = hf_api_query({
"inputs": template_txt,
"parameters": {
'temperature': GlobalConfig.LLM_MODEL_TEMPERATURE,
'min_length': GlobalConfig.LLM_MODEL_MIN_OUTPUT_LENGTH,
'max_length': GlobalConfig.LLM_MODEL_MAX_OUTPUT_LENGTH,
'max_new_tokens': GlobalConfig.LLM_MODEL_MAX_OUTPUT_LENGTH,
'num_return_sequences': 1,
'return_full_text': False,
# "repetition_penalty": 0.0001
},
"options": {
'wait_for_model': True,
'use_cache': True
}
})
output = output[0]['generated_text'].strip()
# output = output[len(template_txt):]
json_end_idx = output.rfind('```')
if json_end_idx != -1:
# logging.debug(f'{json_end_idx=}')
output = output[:json_end_idx]
logging.debug('generate_slides_content: output: %s', output)
return output
if __name__ == '__main__':
# results = get_related_websites('5G AI WiFi 6')
#
# for a_result in results.results:
# print(a_result.title, a_result.url, a_result.extract)
# get_ai_image('A talk on AI, covering pros and cons')
pass
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