Spaces:
Runtime error
Runtime error
File size: 5,060 Bytes
baaf428 c88667a baaf428 c88667a 4332939 baaf428 c88667a baaf428 |
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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
import numpy as np
import pandas as pd
from string import Template
import streamlit as st
import base64
from datasets import load_dataset
from datasets import Dataset
import torch
from tqdm import tqdm
from peft import LoraConfig, get_peft_model
import transformers
# from transformers import AutoModelForCausalLM, AdapterConfig
from transformers import AutoConfig,AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
from transformers import TrainingArguments
from peft import LoraConfig
from peft import *
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
from langchain.prompts import PromptTemplate
from IPython.display import Markdown, display
peft_model_id = "adapter_model.bin"
config = PeftConfig.from_pretrained(peft_model_id)
bnb_config = BitsAndBytesConfig(
load_in_8bit_fp32_cpu_offload=True,
bnb_8bit_use_fp16=False,
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
return_dict=True,
quantization_config=bnb_config,
device_map={"cuda:0": "cpu"},
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
tokenizer.pad_token = tokenizer.eos_token
model = PeftModel.from_pretrained(model, peft_model_id)
prompt_template = """Answer the following multiple choice question by giving the most appropriate response. Answer should be one among [A, B, C, D, E] \
in order of the most likely to be correct to the least likely to be correct.'
Question: {prompt}\n
A) {a}\n
B) {b}\n
C) {c}\n
D) {d}\n
E) {e}\n
Answer: """
prompt = PromptTemplate(template=prompt_template, input_variables=['prompt', 'a', 'b', 'c', 'd', 'e'])
def format_text_to_prompt(example):
ans = prompt.format(prompt=example['prompt'],
a=example['A'],
b=example['B'],
c=example['C'],
d=example['D'],
e=example['E'])
return {"ans": ans}
def get_ans(text):
inputs = tokenizer(text, return_tensors='pt')
logits = model(input_ids=inputs['input_ids'].cuda(), attention_mask=inputs['attention_mask'].cuda()).logits[0, -1]
# Create a list of tuples having (logit, 'option') format
options_list = [(logits[tokenizer(' A').input_ids[-1]], 'A'), (logits[tokenizer(' B').input_ids[-1]], 'B'), (logits[tokenizer(' C').input_ids[-1]], 'C'), (logits[tokenizer(' D').input_ids[-1]], 'D'), (logits[tokenizer(' E').input_ids[-1]], 'E')]
options_list = sorted(options_list, reverse=True)
ans_list = []
for i in range(3):
ans_list.append(options_list[i][1])
return ans_list
def get_base64_of_bin_file(bin_file):
with open(bin_file, 'rb') as f:
data = f.read()
return base64.b64encode(data).decode()
def set_png_as_page_bg(png_file):
img = get_base64_of_bin_file(png_file)
page_bg_img = f"""
<style>
[data-testid="stAppViewContainer"] > .main {{
background-image: url("https://www.tata.com/content/dam/tata/images/verticals/desktop/banner_travel_umaidbhavan_desktop_1920x1080.jpg");
background-size: 200%;
background-position: center;
background-repeat: no-repeat;
background-attachment: local;
}}
[data-testid="stSidebar"] > div:first-child {{
background-image: url("data:image/png;base64,{img}");
background-position: center;
background-repeat: no-repeat;
background-attachment: fixed;
}}
[data-testid="stHeader"] {{
background: rgba(0,0,0,0);
}}
[data-testid="stToolbar"] {{
right: 2rem;
}}
</style>
"""
st.markdown(page_bg_img, unsafe_allow_html=True)
def get_base64_encoded_image(image_path):
with open(image_path, "rb") as img_file:
encoded_string = base64.b64encode(img_file.read()).decode("utf-8")
return encoded_string
def main():
set_png_as_page_bg("net_technology_5407.jpg")
image_path = "artificial-intelligence.jpg" # Replace with the actual image file path
st.title("Sci-mcq-GPT")
link = "https://drive.google.com/file/d/1_2TqNNyoczhxIBmU7BpOzEi2bu3MC-sx/view?usp=sharing"
icon_path = "pdf download logo.png"
encoded_image = get_base64_encoded_image(icon_path)
lnk = f'<a href="{link}"><img src="data:image/png;base64,{encoded_image}" width="50" height="50"></a>'
col = st.sidebar
col.markdown(lnk, unsafe_allow_html=True)
st.subheader("Ask Q&A")
col1, col2 = st.columns(2)
query = col1.text_area("Enter your question")
if col1.button("Get Answer"):
ans = get_ans(query)
print(ans)
col2.text_area("Sci-mcq-GPT Response", ans)
else:
col2.text_area("Sci-mcq-GPT Response", value="")
col_sidebar = st.sidebar
col_sidebar.image(image_path, caption=" ", width=300)
if __name__ == "__main__":
main() |