chatt5-small.py

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load the fine-tuned model and tokenizer
model_path = "./t5-small-finetuned"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)

# Ensure model is in evaluation mode
model.eval()

def chat_with_model(prompt):
    # Encode the input text
    input_ids = tokenizer.encode(prompt, return_tensors='pt')

    # Generate a response
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_length=150,  # Adjust as needed
            num_beams=5,     # Use beam search for better results
            early_stopping=True
        )

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

def main():
    print("Chatbot is running. Type 'exit' to end the conversation.")
    
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'exit':
            break
        
        response = chat_with_model(user_input)
        print(f"Bot: {response}")

if __name__ == "__main__":
    main()