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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| import spaces | |
| # Load the model and tokenizer from Hugging Face | |
| model_path = "Ozaii/W.AI-13B-Chat" # Replace with your username and repository name | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| model = AutoModelForCausalLM.from_pretrained(model_path) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| model.eval() | |
| def generate_response(user_input, chat_history): | |
| max_context_length = 750 | |
| max_response_length = 150 | |
| prompt = "" | |
| for message in chat_history: | |
| if message[0] is not None: | |
| prompt += f"User: {message[0]}\n" | |
| if message[1] is not None: | |
| prompt += f"Assistant: {message[1]}\n" | |
| prompt += f"***Use natural conversation style of assistant, you can use +18 swear words and use irregular language similar to assistant.***User: {user_input}\nAssistant:" | |
| prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False) | |
| if len(prompt_tokens) > max_context_length: | |
| prompt_tokens = prompt_tokens[-max_context_length:] | |
| prompt = tokenizer.decode(prompt_tokens, clean_up_tokenization_spaces=True) | |
| inputs = tokenizer(prompt, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| inputs.input_ids, | |
| max_length=len(inputs.input_ids[0]) + max_response_length, # Limit the maximum length for context and response | |
| min_length=45, | |
| temperature=0.7, # Slightly higher temperature for more diverse responses | |
| top_k=30, | |
| top_p=0.9, # Allow a bit more randomness | |
| repetition_penalty=1.1, # Mild repetition penalty | |
| no_repeat_ngram_size=3, # Ensure no repeated phrases | |
| eos_token_id=tokenizer.eos_token_id, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| assistant_response = response.split("Assistant:")[-1].strip() | |
| assistant_response = assistant_response.split('\n')[0].strip() | |
| chat_history.append((user_input, assistant_response)) | |
| return chat_history, chat_history | |
| def restart_chat(): | |
| return [], [] | |
| with gr.Blocks() as chat_interface: | |
| gr.Markdown("<h1><center>W.AI Chat Nikker xD</center></h1>") | |
| chat_history = gr.State([]) | |
| with gr.Column(): | |
| chatbox = gr.Chatbot() | |
| with gr.Row(): | |
| user_input = gr.Textbox(show_label=False, placeholder="Summon Wali Here...") | |
| submit_button = gr.Button("Send") | |
| restart_button = gr.Button("Restart") | |
| submit_button.click( | |
| generate_response, | |
| inputs=[user_input, chat_history], | |
| outputs=[chatbox, chat_history] | |
| ) | |
| restart_button.click( | |
| restart_chat, | |
| inputs=[], | |
| outputs=[chatbox, chat_history] | |
| ) | |
| chat_interface.launch(share=True) | |