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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Replace with the model name you are using
model_name = "facebook/blenderbot-400M-distill"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Function to generate bot response
def generate_response(user_input):
inputs = tokenizer(user_input, return_tensors="pt")
bot_output = model.generate(**inputs)
bot_response = tokenizer.decode(bot_output[0], skip_special_tokens=True)
return bot_response
# Example conversation flow
conversation_history = []
while True:
user_input = input("User: ")
conversation_history.append(user_input)
if user_input.lower() == "exit":
break
bot_response = generate_response(user_input)
conversation_history.append(f"Bot: {bot_response}")
print(f"Bot: {bot_response}")
print("Updated Conversation History:", conversation_history)
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