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import gradio as gr
import random
import time
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"manan05/mistral-7b-friends-v0.1.gguf",
model_file="mistralfriends-7b-v0.1.gguf",
model_type="mistral",
gpu_layers=0,
hf=True
)
from transformers import AutoTokenizer, pipeline
# Tokenizer
tokenizer = AutoTokenizer.from_pretrained("manan05/mistral-7b-friends")
# Pipeline
generator = pipeline(
model=model, tokenizer=tokenizer,
task='text-generation',
max_new_tokens=50,
repetition_penalty=1.1,
return_full_text=False
)
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.ClearButton([msg, chatbot])
def respond(message, chat_history):
temp_chat_history = []
for c in chat_history:
temp_chat_history.append('\n'.join(c))
print('\n'.join(temp_chat_history))
user_message = "Given the following conversation context, generate the upcomming dialogue of Joey in his style. \n CONTEXT: Me: " + '\n'.join(temp_chat_history) + '\n' + message
temp_bot_message = generator(user_message)[0]["generated_text"].split('\n')
for i in temp_bot_message:
if("Joey:" in i):
bot_message = i
break;
chat_history.append(("Me: " + message, bot_message))
time.sleep(2)
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
demo.launch() |