from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gpt2" # Replace with a larger model if desired tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

def chat_with_ai(user_input): inputs = tokenizer.encode(user_input, return_tensors="pt") outputs = model.generate(inputs, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response

print("Chat with your AI (type 'exit' to quit)") while True: user_input = input("You: ") if user_input.lower() == 'exit': break response = chat_with_ai(user_input) print(f"AI: {response}")

from transformers import AutoModelForCausalLM, AutoTokenizer

app = Flask(name)

model_name = "gpt2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

@app.route('/chat', methods=['POST']) def chat(): user_input = request.json.get('message', '') if not user_input: return jsonify({"response": "Please provide a message!"})

inputs = tokenizer.encode(user_input, return_tensors="pt")
outputs = model.generate(inputs, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return jsonify({"response": response})

if name == "main": app.run(debug=True)