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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
model_path = "Ozaii/TinyWali1.1B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
# Ensure the model is in evaluation mode and on the correct device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
# Define Generation Parameters and Function with Enhanced Context Management
def generate_response(user_input, chat_history):
max_context_length = 750 # Specify the maximum context length
max_response_length = 150 # Specify the maximum response length
# Prepare the prompt with chat history
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"User: {user_input}\nAssistant:"
# Ensure the context does not exceed the maximum context length
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)
# Generate response
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)
# Post-process the assistant's response
assistant_response = response.split("Assistant:")[-1].strip()
# Ensure the response ends properly by stripping incomplete sentences
assistant_response = assistant_response.split('\n')[0].strip()
# Append the interaction to the chat history
chat_history.append((user_input, assistant_response))
# Return the updated chat history
return chat_history, chat_history
def restart_chat():
return [], []
# Create Gradio Interface
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]
)
# Launch the Gradio interface with share=True
chat_interface.launch(share=True)
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