from transformers import GPT3Tokenizer, GPT3LMHeadModel

# Load the tokenizer
tokenizer = GPT3Tokenizer.from_pretrained("gpt-3.5-turbo")

# Load the model
model = GPT3LMHeadModel.from_pretrained("gpt-3.5-turbo")

# Define the conversation loop
while True:
    # Capture user input
    user_input = input("User: ")

    # Format user input as prompts
    prompts = ["User: " + user_input]

    # Generate model response
    model_output = model.generate(tokenizer.encode(prompts, return_tensors="pt"), max_length=100)

    # Extract and display model-generated response
    model_response = tokenizer.decode(model_output[0], skip_special_tokens=True)
    print("Bot: " + model_response)

    from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/chatbot', methods=['POST'])
def chatbot():
    user_input = request.json['user_input']
    prompts = ["User: " + user_input]
    model_output = model.generate(tokenizer.encode(prompts, return_tensors="pt"), max_length=100)
    model_response = tokenizer.decode(model_output[0], skip_special_tokens=True)
    return jsonify({'bot_response': model_response})

if __name__ == '__main__':
    app.run(debug=True)