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import time
import gradio as gr
from ctransformers import AutoModelForCausalLM
from spellchecker import SpellChecker
llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardLM-7B-uncensored-GGUF", model_file="WizardLM-7B-uncensored.Q3_K_M.gguf", model_type="llama", stream=True)
history = [""]
spell = SpellChecker()
def correct_words(text):
words = text.split()
corrected_words = [spell.correction(word) for word in words]
corrected_text = ' '.join(corrected_words)
return corrected_text
import asyncio
import asyncio
# ... (previous code)
async def generate_async(tokens):
for token in tokens:
yield token
async def generate_response_async(message):
global history
for _ in range(2):
tokens = [ord(char) for char in message]
response = llm.generate_async(tokens=tokens, top_k=50, top_p=0.95, temperature=1.0, repetition_penalty=1.0, last_n_tokens=1)
response_text = ' '.join(map(str, await asyncio.gather(*response)))
time.sleep(2)
corrected_response = correct_words(response_text)
history.append(corrected_response)
yield ' '.join(history)
history = ["Chatbot:"]
# Clear the history list after the last response
history = ["Chatbot:"]
def chatbot(message, history):
response_generator = generate_response_async(message)
for response in response_generator:
time.sleep(0.1) # Optional delay for a natural chat feel
yield response
iface = gr.ChatInterface(chatbot)
iface.launch() |