File size: 2,201 Bytes
8c422df 124eea0 8c422df 124eea0 8c422df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | """CARDS / Debunk APIs"""
from pathlib import Path
from langchain_core.prompts import PromptTemplate
from pydantic import BaseModel, computed_field
from ..llm.llms import google_llm
# Construct the path to the cards.md file
current_file_path = Path(__file__).resolve()
prompts_dir = current_file_path.parent.parent / "prompts"
cards_prompt_path = prompts_dir / "cards.md"
cards_prompt = cards_prompt_path.read_text()
# print(google_llm.profile)
CATEGORY_NAMES = {
"0": "Not climate misinformation",
"1.1": "Ice/permafrost/snow cover isn't melting",
"1.2": "We're heading into an ice age/global cooling",
"1.3": "Weather is cold/snowing",
"1.4": "Climate hasn't warmed/changed over the last (few) decade(s)",
"1.6": "Sea level rise is exaggerated/not accelerating",
"1.7": "Extreme weather isn't increasing/has happened before/isn't linked to climate change",
"2.1": "It's natural cycles/variation",
"2.2": "There's no evidence for greenhouse effect/carbon dioxide driving climate change",
"3.1": "Climate sensitivity is low/negative feedbacks reduce warming",
"3.2": "Species/plants/reefs aren't showing climate impacts/are benefiting from climate change",
"3.3": "CO2 is beneficial/not a pollutant",
"4.1": "Climate policies (mitigation or adaptation) are harmful",
"4.2": "Climate policies are ineffective/flawed",
"4.4": "Clean energy technology/biofuels won't work",
"4.5": "People need energy (e.g. from fossil fuels/nuclear)",
"5.1": "Climate-related science is unreliable/uncertain/unsound (data, methods & models)",
"5.2": "Climate movement is unreliable/alarmist/corrupt",
}
class CategoryResponse(BaseModel):
category: str
@computed_field
@property
def description(self) -> str:
return CATEGORY_NAMES.get(self.category, "Unknown category")
def classify_text(text: str) -> CategoryResponse:
prompt = PromptTemplate.from_template(
cards_prompt + "\n\nUser Query:\n{query}\n\nResponse:"
)
llm_with_structure = google_llm.with_structured_output(CategoryResponse)
chain = prompt | llm_with_structure
response = chain.invoke({"query": text})
return response
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