Create Bot

Bot

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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