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from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
from global_config import GlobalConfig
CHANNEL = ClarifaiChannel.get_grpc_channel()
STUB = service_pb2_grpc.V2Stub(CHANNEL)
METADATA = (
('authorization', 'Key ' + GlobalConfig.CLARIFAI_PAT),
)
USER_DATA_OBJECT = resources_pb2.UserAppIDSet(
user_id=GlobalConfig.CLARIFAI_USER_ID,
app_id=GlobalConfig.CLARIFAI_APP_ID
)
RAW_TEXT = '''You are a helpful, intelligent chatbot. Create the slides for a presentation on the given topic. Include main headings for each slide, detailed bullet points for each slide. Add relevant content to each slide. Do not output any blank line.
Topic:
Talk about AI, covering what it is and how it works. Add its pros, cons, and future prospects. Also, cover its job prospects.
'''
def get_text_from_llm(prompt: str) -> str:
post_model_outputs_response = STUB.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=USER_DATA_OBJECT, # The userDataObject is created in the overview and is required when using a PAT
model_id=GlobalConfig.CLARIFAI_MODEL_ID,
# version_id=MODEL_VERSION_ID, # This is optional. Defaults to the latest model version
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
text=resources_pb2.Text(
raw=prompt
)
)
)
]
),
metadata=METADATA
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print(post_model_outputs_response.status)
raise Exception(f"Post model outputs failed, status: {post_model_outputs_response.status.description}")
# Since we have one input, one output will exist here
output = post_model_outputs_response.outputs[0]
# print("Completion:\n")
# print(output.data.text.raw)
return output.data.text.raw
if __name__ == '__main__':
topic = ('Talk about AI, covering what it is and how it works.'
' Add its pros, cons, and future prospects.'
' Also, cover its job prospects.'
)
print(topic)
with open(GlobalConfig.SLIDES_TEMPLATE_FILE, 'r') as in_file:
prompt_txt = in_file.read()
prompt_txt = prompt_txt.replace('{topic}', topic)
response_txt = get_text_from_llm(prompt_txt)
print('Output:\n', response_txt)
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