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