chatbot.py

# Chatbot with LLMs

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# For this example, you'll be using facebook/blenderbot-400M-distill 
# because it has an open-source license and runs relatively fast.
model_name = "facebook/blenderbot-400M-distill"

# Fetch the model and initialize a tokenizer
# two terms: model and tokenizer.

# Load model (download on first run and reference local installation for consequent runs)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# CHAT

# Keeping track of conversation history
# Initialize it.
conversation_history = []

# Encoding the conversation history 
history_string = "\n".join(conversation_history)

# Fetch prompt from user
input_text ="hello, how are you doing?"

# Tokenization of user prompt and chat history
inputs = tokenizer.encode_plus(history_string, input_text, return_tensors="pt")
print(inputs)

tokenizer.pretrained_vocab_files_map

# Generate output from the model
outputs = model.generate(**inputs)
print(outputs)

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

# Update conversation history
conversation_history.append(input_text)
conversation_history.append(response)
print(conversation_history)

while True:
    # Create conversation history string
    history_string = "\n".join(conversation_history)

    # Get the input data from the user
    input_text = input("> ")

    # Tokenize the input text and history
    inputs = tokenizer.encode_plus(history_string, input_text, return_tensors="pt")

    # Generate the response from the model
    outputs = model.generate(**inputs)

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

    # Add interaction to conversation history
    conversation_history.append(input_text)
    conversation_history.append(response)