CHST.PY

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# Load pre-trained model and tokenizer
model_name = "gpt2"  # You can use other models like "gpt2-medium", "gpt2-large", etc.
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Add special tokens if using specific fine-tuned models
tokenizer.pad_token = tokenizer.eos_token
model.resize_token_embeddings(len(tokenizer))

# Device configuration (use GPU if available)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Function to generate AI response
def get_ai_response(user_input):
    # Tokenize input and encode it
    inputs = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt").to(device)

    # Generate response (max_length can be adjusted)
    outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2, top_p=0.95, temperature=0.7)

    # Decode response
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Main chatbot loop
print("AI Chatbot is ready to talk! Type 'quit' to end the conversation.")
while True:
    user_input = input("You: ")
    
    # Exit condition
    if user_input.lower() == 'quit':
        print("AI Chatbot: Goodbye!")
        break
    
    # Get AI response
    ai_response = get_ai_response(user_input)
    print(f"AI Chatbot: {ai_response}")