Compare and contrast the two programs below.
Gemini script:
```
import json
import pandas as pd
from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
import torch

# --- 1. Data Loading and Preparation ---

# Load the full dataset
with open("balanced_labeled_personality_conversations_cleaned.json", "r") as f:
    data = json.load(f)

# Create a DataFrame and filter for our target traits
df = pd.DataFrame(data)
traits = ["witty", "sarcastic", "charming"]
df = df[df['trait'].isin(traits)]

# --- 2. Model and Tokenizer Setup for MoE ---

# Use a real MoE model from DeepSeek. This one has 16B total parameters.
# Note: This is a large model and will require significant GPU resources.
model_checkpoint = "deepseek-ai/deepseek-moe-16b-base"

# trust_remote_code is necessary for many new model architectures
# torch_dtype=torch.bfloat16 is used for memory efficiency
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_checkpoint,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    # device_map="auto" helps distribute the model across available GPUs
    device_map="auto"
)

# It's good practice to add a padding token if the tokenizer doesn't have one
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# --- 3. Formatting and Tokenization for Guided Specialization ---

# We add a "control token" to the prompt to guide the MoE router.
# The model will learn to associate "[EXPERT: WITTY]" with a witty response style.
def format_conversation_for_moe(example):
    # The trait is converted to uppercase to make the control token distinct.
    trait_token = f"[EXPERT: {example['trait'].upper()}]"
    return f"{trait_token} 
 {example['dialogue']}\n
"

# Apply the new formatting to the entire DataFrame
print("Formatting dataset for MoE training...")
df["text"] = df.apply(format_conversation_for_moe, axis=1)

# Tokenization function remains largely the same
def tokenize_data(data):
    return tokenizer(
        data["text"],
        truncation=True,
        # A longer max_length might be beneficial depending on your data
        max_length=512,
        padding="max_length",
    )

# Create a single dataset from the combined and formatted pandas DataFrame
print("Creating and tokenizing the unified dataset...")
full_dataset = Dataset.from_pandas(df[["text"]])
tokenized_dataset = full_dataset.map(tokenize_data, batched=True, remove_columns=["text"])


# --- 4. Training the Single MoE Model ---

print("\n🚀 Training the unified MoE model...")

# Training arguments are configured for the single training run.
# Note: You will likely need to adjust batch size based on your GPU memory.
# Using gradient_accumulation_steps can help simulate a larger batch size.
training_args = TrainingArguments(
    output_dir="./moe_specialized_model",
    per_device_train_batch_size=1,  # Lower this if you run out of memory
    gradient_accumulation_steps=4,  # Effective batch size = 1 * 4 = 4
    num_train_epochs=1,
    save_steps=500,
    save_total_limit=1,
    logging_steps=50,
    logging_dir="./logs_moe",
    # The aux_loss is crucial for MoE models to ensure expert load balancing
    label_names=["input_ids", "attention_mask"],
    report_to="none" # Disables reporting to services like wandb
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    tokenizer=tokenizer,
    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
)

# This single train() call fine-tunes the model on all personalities at once,
# guided by the control tokens.
trainer.train()

print("✅ Finished training the unified MoE model.")

# Save the final model and tokenizer
final_model_path = "./moe_specialized_model/final"
model.save_pretrained(final_model_path)
tokenizer.save_pretrained(final_model_path)
print(f"✅ Model saved to {final_model_path}")


# --- 5. Inference with the Specialized MoE Model ---
from transformers import pipeline

def generate_moe_response(trait, user_input, model, tokenizer):
    """
    Generates a response from the single, specialized MoE model.
    """
    trait = trait.lower()
    if trait not in traits:
        return f"Error: Trait '{trait}' is not valid. Use one of {traits}."

    # Construct the prompt with the correct control token to guide the model
    trait_token = f"[EXPERT: {trait.upper()}]"
    prompt = f"{trait_token} 
 {user_input}\n
"

    # Create the pipeline on the fly
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
    result = pipe(prompt, max_new_tokens=100, do_sample=True, temperature=0.7)
    return result[0]["generated_text"]

# Example Usage (after training is complete)
print("\n--- Testing Inference ---")
# You would load the final model before running inference like this:
# final_model_path = "./moe_specialized_model/final"
# loaded_tokenizer = AutoTokenizer.from_pretrained(final_model_path)
# loaded_model = AutoModelForCausalLM.from_pretrained(final_model_path, device_map="auto", torch_dtype=torch.bfloat16)

# For demonstration, we use the model already in memory
sarcastic_response = generate_moe_response("sarcastic", "What do you think about online dating?", model, tokenizer)
print(f"\nSarcastic Expert Response:\n{sarcastic_response}")

charming_response = generate_moe_response("charming", "I had a really rough day.", model, tokenizer)
print(f"\nCharming Expert Response:\n{charming_response}")
```
ChatGPT script:
```
import json
import pandas as pd
from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling

# -----------------------------
# Step 1: Load and preprocess data
# -----------------------------

with open("balanced_labeled_personality_conversations_cleaned.json", "r") as f:
    data = json.load(f)

df = pd.DataFrame(data)
df = df[df['trait'].isin(["witty", "sarcastic", "charming"])].dropna(subset=["dialogue"])

# Trait-based prefix injection
trait_prefix = {
    "witty": "Respond with clever wit and humor.",
    "sarcastic": "Reply using a sharp, dry sarcastic tone.",
    "charming": "Speak in a warm and charismatic manner."
}

# Format prompt: inject personality guide
def format_conversation(row):
    style = trait_prefix.get(row["trait"], "")
    return f"
 {style} {row['dialogue']}\n
"

df["text"] = df.apply(format_conversation, axis=1)

# -----------------------------
# Step 2: Tokenization
# -----------------------------

model_checkpoint = "deepseek-ai/deepseek-moe-16b-base"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, trust_remote_code=True)

def tokenize_data(batch):
    return tokenizer(
        batch["text"],
        padding="max_length",
        truncation=True,
        max_length=512,
    )

dataset = Dataset.from_pandas(df[["text"]])
tokenized_dataset = dataset.map(tokenize_data, batched=True, remove_columns=["text"])

# -----------------------------
# Step 3: Load model & train
# -----------------------------

model = AutoModelForCausalLM.from_pretrained(model_checkpoint, trust_remote_code=True)

training_args = TrainingArguments(
    output_dir="./deepseek_moe_finetuned_traits",
    per_device_train_batch_size=2,
    per_device_eval_batch_size=2,
    num_train_epochs=1,
    save_steps=500,
    logging_dir="./logs",
    logging_steps=100,
    evaluation_strategy="no",
    report_to="none"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    tokenizer=tokenizer,
    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)
)

trainer.train()

# Save model
trainer.save_model("./deepseek_moe_finetuned_traits")
tokenizer.save_pretrained("./deepseek_moe_finetuned_traits")

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# -----------------------------
# Step 1: Load fine-tuned model
# -----------------------------
model_path = "./deepseek_moe_finetuned_traits"  # Path from training script
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
model.eval().to("cuda" if torch.cuda.is_available() else "cpu")

# -----------------------------
# Step 2: Trait prefix definitions
# -----------------------------
trait_prefix = {
    "witty": "Respond with clever wit and humor.",
    "sarcastic": "Reply using a sharp, dry sarcastic tone.",
    "charming": "Speak in a warm and charismatic manner."
}

# -----------------------------
# Step 3: Generate response from selected trait
# -----------------------------
def generate_response(user_input, trait, max_new_tokens=100):
    if trait not in trait_prefix:
        raise ValueError("Trait must be one of: witty, sarcastic, charming")

    prompt = f"
 {trait_prefix[trait]} {user_input}\n
"

    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
    result = pipe(prompt, max_new_tokens=max_new_tokens, do_sample=True, top_p=0.9, temperature=0.8)
    
    # Remove the prompt part and only return the assistant's reply
    generated = result[0]['generated_text']
    response = generated.split("
")[-1].strip()
    return response

# -----------------------------
# Step 4: Interactive CLI
# -----------------------------
if __name__ == "__main__":
    print("🧠 DeepSeek-MoE | Personality AI Assistant")
    print("Choose a trait: witty | sarcastic | charming")
    
    while True:
        trait = input("\nSelect trait (or type 'exit'): ").strip().lower()
        if trait == "exit":
            break
        if trait not in trait_prefix:
            print("❌ Invalid trait. Try again.")
            continue

        user_input = input("You: ").strip()
        response = generate_response(user_input, trait)
        print(f"\n{trait.capitalize()} AI: {response}")
```