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| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import gradio as gr | |
| import torch | |
| title = "🤖AI ChatBot" | |
| description = "A State-of-the-Art Large-scale Pretrained Response generation model (gpt-neo-1.3B)" | |
| examples = [["How are you?"]] | |
| # Use the better model and tokenizer | |
| model_name = "EleutherAI/gpt-neo-1.3B" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| def predict(input_text, history=None): | |
| if history is None: | |
| history = [] | |
| # Tokenize the new input sentence | |
| new_user_input_ids = tokenizer.encode( | |
| input_text + tokenizer.eos_token, return_tensors="pt" | |
| ) | |
| # Append the new user input tokens to the chat history | |
| bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
| # Generate a response using batch processing | |
| generated_ids = model.generate( | |
| bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id | |
| ) | |
| # Convert the generated response tokens to text | |
| response = tokenizer.decode(generated_ids[0], skip_special_tokens=True) | |
| # Split the responses into lines | |
| response = response.split("\n") | |
| # Convert to tuples of list | |
| response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)] | |
| return response, generated_ids.tolist() | |
| gr.Interface( | |
| fn=predict, | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| inputs=["text", "state"], | |
| outputs=["chatbot", "state"], | |
| theme="finlaymacklon/boxy_violet", | |
| ).launch() | |