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import json
from utils.paper_retriever import RetrieverFactory
from utils.llms_api import APIHelper
from utils.header import ConfigReader
from generator import IdeaGenerator
class Backend(object):
def __init__(self) -> None:
CONFIG_PATH = "./configs/datasets.yaml"
EXAMPLE_PATH = "./assets/data/example.json"
USE_INSPIRATION = True
BRAINSTORM_MODE = "mode_c"
self.config = ConfigReader.load(CONFIG_PATH)
RETRIEVER_NAME = self.config.RETRIEVE.retriever_name
self.api_helper = APIHelper(self.config)
self.retriever_factory = RetrieverFactory.get_retriever_factory().create_retriever(
RETRIEVER_NAME,
self.config
)
self.idea_generator = IdeaGenerator(self.config, None)
self.use_inspiration = USE_INSPIRATION
self.brainstorm_mode = BRAINSTORM_MODE
self.examples = self.load_examples(EXAMPLE_PATH)
def load_examples(self, path):
try:
with open(path, "r") as f:
data = json.load(f)
return data
except (FileNotFoundError, json.JSONDecodeError) as e:
print(f"Error loading examples from {path}: {e}")
return []
def background2brainstorm_callback(self, background, json_strs=None):
if json_strs is not None: # only for DEBUG_MODE
json_contents = json.loads(json_strs)
return json_contents["brainstorm"]
else:
return self.api_helper.generate_brainstorm(background)
def brainstorm2entities_callback(self, background, brainstorm, json_strs=None):
if json_strs is not None: # only for DEBUG_MODE
json_contents = json.loads(json_strs)
entities_bg = json_contents["entities_bg"]
entities_bs = json_contents["entities_bs"]
entities_all = entities_bg + entities_bs
# return gr.CheckboxGroup(choices=entities, value=entities, label="Expanded key words", visible=True)
return entities_all
else:
entities_bg = self.api_helper.generate_entity_list(background)
entities_bs = self.api_helper.generate_entity_list(brainstorm, 10)
entities_all = list(set(entities_bg) | set(entities_bs))
# return extracted_entities
# return gr.CheckboxGroup(choices=entities_all, value=entities_all, label="Expanded key words", visible=True)
return entities_all
def upload_json_callback(self, input):
# print(type(input))
# print(len(input))
# print(input) # temp file path
with open(input, "r") as json_file:
contents = json_file.read()
json_contents = json.loads(contents)
return [json_contents["background"], contents]
def entities2literature_callback(self, background, entities, json_strs=None):
if json_strs is not None:
result = json.loads(json_strs)
res = []
for i, p in enumerate(result["related_paper"]):
res.append(str(p))
else:
result = self.retriever_factory.retrieve(background, entities, need_evaluate=False, target_paper_id_list=[])
res = []
for i, p in enumerate(result["related_paper"]):
res.append(f'{p["title"]}. {p["venue_name"].upper()} {p["year"]}.')
return res, result["related_paper"]
def literature2initial_ideas_callback(self, background, brainstorms, retrieved_literature, json_strs=None):
if json_strs is not None:
json_contents = json.loads(json_strs)
return json_contents["median"]["initial_idea"]
else:
self.idea_generator.paper_list = retrieved_literature
self.idea_generator.brainstorm = brainstorms
if self.use_inspiration:
message_input, idea_modified, median = (
self.idea_generator.generate_by_inspiration(
background, "new_idea", self.brainstorm_mode, False)
)
else:
message_input, idea_modified, median = self.idea_generator.generate(
background, "new_idea", self.brainstorm_mode, False
)
return median["initial_idea"], idea_modified
def initial2final_callback(self, initial_ideas, final_ideas, json_strs=None):
if json_strs is not None:
json_contents = json.loads(json_strs)
return json_contents["median"]["modified_idea"]
else:
return final_ideas
def get_demo_i(self, i):
if 0 <= i < len(self.examples):
return self.examples[i].get("background", "Background not found.")
else:
return "Example not found. Please select a valid index."
# return ("The application scope of large-scale language models such as GPT-4 and LLaMA "
# "has rapidly expanded, demonstrating powerful capabilities in natural language processing "
# "and multimodal tasks. However, as the size and complexity of the models increase, understanding "
# "how they make decisions becomes increasingly difficult. Challenge: 1 The complexity of model "
# "interpretation: The billions of parameters and nonlinear decision paths within large-scale language "
# "models make it very difficult to track and interpret specific outputs. The existing interpretation "
# "methods usually only provide a local perspective and are difficult to systematize. 2. Transparency "
# "and Fairness: In specific scenarios, models may exhibit biased or discriminatory behavior. Ensuring "
# "the transparency of these models, reducing bias, and providing credible explanations is one of the current challenges.")
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