Spaces:
Runtime error
Runtime error
File size: 1,602 Bytes
28d9ec7 b3fbb05 28d9ec7 e1452b8 28d9ec7 b3fbb05 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
"""
import gradio as gr
def mental_chat(message, history):
return givetext(patienttext,newmodel,newtokenizer)
demo = gr.ChatInterface(mental_chat)
demo.launch()
"""
import gradio as gr
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# ##### ##### ##### ##### #####
peft_model_id = "charansr/llama2-7b-chat-hf-therapist"
config = PeftConfig.from_pretrained(peft_model_id)
newmodel = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
newtokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
newmodel = PeftModel.from_pretrained(newmodel, peft_model_id)
def givetext(input_text,lmodel,ltokenizer):
eval_prompt_pt1 = """\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction: Act like a therapist and respond\n\n### Input: """
eval_prompt_pt2="""\n\n\n### Response:\n"""
eval_prompt=eval_prompt_pt1+input_text+eval_prompt_pt2
print(eval_prompt,"\n\n")
model_input = ltokenizer(eval_prompt, return_tensors="pt").to("cuda")
lmodel.eval()
with torch.no_grad():
return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=1000)[0], skip_special_tokens=True))
#return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
def mental_chat(message, history):
return givetext(patienttext,newmodel,newtokenizer)
demo = gr.ChatInterface(mental_chat)
demo.launch() |