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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()]
# Simulate generations with learning, interaction
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