license: openrail
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from rdflib import Graph from datasets import load_dataset from transformers import pipeline from transformers import TextQueryProcessor, QuestionAnswerer from gradio import Interface
Define specializations and subfields
SPECIALIZATIONS = { "Science": {"subfields": ["Physics", "Biology", "Chemistry"]}, "History": {"subfields": ["Ancient", "Medieval", "Modern"]}, "Art": {"subfields": ["Literature", "Visual", "Music"]}, }
Define knowledge graph for each specialization
knowledge_graphs = { specialization: Graph() for specialization in SPECIALIZATIONS.keys() }
Define Hugging Face models and pipelines
model_names = { "Physics": "allenai/bart-large-cc2", "Biology": "bert-base-uncased-finetuned-squad", "Chemistry": "allenai/biobert-base", "Ancient": "facebook/bart-base-uncased-cnn", "Medieval": "distilbert-base-uncased-finetuned-squad", "Modern": "allenai/longformer-base-4096", "Literature": "gpt2-large", "Visual": "autoencoder/bart-encoder", "Music": "openai/music-gpt", }
models = { specialization: AutoModelForSeq2SeqLM.from_pretrained(model_names[specialization]) for specialization in model_names.keys() }
tokenizers = { specialization: AutoTokenizer.from_pretrained(model_names[specialization]) for specialization in model_names.keys() }
qa_processor = TextQueryProcessor.from_pretrained("allenai/bart-large") qa_model = QuestionAnswerer.from_pretrained("allenai/bart-large")
Generation pipeline for creative text formats
generation_pipeline = pipeline("text-generation", model="gpt2", top_k=5)
Interactive interface
interface = Interface( fn=interact, inputs=["text", "specialization"], outputs=["text"], title="AI Chatbot Civilization", description="Interact with a generation of chatbots!", )
def interact(text, specialization): """Interact with a chatbot based on prompt and specialization.""" # Choose a chatbot from the current generation chatbot = Chatbot(specialization)
# Process the prompt and identify relevant knowledge
processed_prompt = process_prompt(text, specialization)
# Generate response using specialization model
response = models[specialization].generate(
input_ids=tokenizers[specialization](
processed_prompt, return_tensors="pt"
).input_ids
)
# Check for knowledge graph consultation request
if response.sequences[0].decode() == "Consult":
# Use QA model and knowledge graph to answer question
answer = qa_model(qa_processor(text, knowledge_graphs[specialization]))
return answer["answer"]
# Use generation pipeline for creative formats
if need_creative_format(text):
return generation_pipeline(text, max_length=50)
return response.sequences[0].decode()
def process_prompt(text, specialization): """Preprocess prompt based on specialization and subfield.""" # Use subfield-specific data and techniques here # Example: extract chemical equations for "Chemistry" prompts return text
def need_creative_format(text): """Check if prompt requires creative text generation.""" # Use keywords, patterns, or other techniques to identify # Example: "Write a poem about..." or "Compose a melody like..." return False
def learn(data, specialization): """Update knowledge graph and fine-tune model based on data.""" # Use RDF and Hugging Face datasets/fine-tuning techniques # Update knowledge_graphs and models dictionaries pass
def mutate(chatbot): """Create a new chatbot with potentially mutated specialization.""" # Implement logic for specialization mutation based on generation # Update chatbot.specialization and potentially subfield pass
Generate the first generation
chatbots = [Chatbot(specialization) for specialization in SPECIALIZATIONS.keys()]