File size: 11,473 Bytes
fb4710e 099741f fb4710e 862259b fb4710e ca71749 fb4710e 099741f fb4710e bd28dd7 fb4710e 099741f fb4710e bd28dd7 54d5e6e bd28dd7 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f ca71749 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f fb4710e 099741f ca71749 099741f ca71749 099741f ca71749 099741f 9c8e6da 099741f ca71749 099741f ca71749 099741f ca71749 099741f ca71749 099741f ca71749 9c8e6da fb4710e 9c8e6da ca71749 9c8e6da 099741f 9c8e6da ca71749 9c8e6da ca71749 9c8e6da ca71749 fb4710e ca71749 862259b ca71749 fb4710e ca71749 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 |
from datetime import datetime
from dotenv import load_dotenv
from img2table.document import Image
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.reduce import ReduceDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_openai import ChatOpenAI
from pdf2image import convert_from_path
from prompt import prompt_entity_gsd_chunk, prompt_entity_gsd_combine, prompt_entity_summ_chunk, prompt_entity_summ_combine, prompt_entities_chunk, prompt_entities_combine, prompt_entity_one_chunk, prompt_table, prompt_validation
from table_detector import detection_transform, device, model, ocr, outputs_to_objects
import io
import json
import os
import pandas as pd
import re
import requests
import time
import torch
load_dotenv()
prompts = {
'gsd': [prompt_entity_gsd_chunk, prompt_entity_gsd_combine],
'summ': [prompt_entity_summ_chunk, prompt_entity_summ_combine],
'all': [prompt_entities_chunk, prompt_entities_combine]
}
class Process():
def __init__(self, llm, llm_val):
if llm.startswith('gpt'):
self.llm = ChatOpenAI(temperature=0, model_name=llm)
elif llm.startswith('gemini'):
self.llm = ChatGoogleGenerativeAI(temperature=0, model=llm)
else:
self.llm = ChatOpenAI(temperature=0, model_name=llm, api_key=os.environ['PERPLEXITY_API_KEY'], base_url="https://api.perplexity.ai")
if llm_val.startswith('gpt'):
self.llm_val = ChatOpenAI(temperature=0, model_name=llm_val)
elif llm_val.startswith('gemini'):
self.llm_val = ChatGoogleGenerativeAI(temperature=0, model=llm_val)
else:
self.llm_val = ChatOpenAI(temperature=0, model_name=llm_val, api_key=os.environ['PERPLEXITY_API_KEY'], base_url="https://api.perplexity.ai")
def get_entity(self, data):
chunks, types = data
map_template = prompts[types][0]
map_prompt = PromptTemplate.from_template(map_template)
map_chain = LLMChain(llm=self.llm, prompt=map_prompt)
reduce_template = prompts[types][1]
reduce_prompt = PromptTemplate.from_template(reduce_template)
reduce_chain = LLMChain(llm=self.llm, prompt=reduce_prompt)
combine_chain = StuffDocumentsChain(
llm_chain=reduce_chain, document_variable_name="doc_summaries"
)
reduce_documents_chain = ReduceDocumentsChain(
combine_documents_chain=combine_chain,
collapse_documents_chain=combine_chain,
token_max=100000,
)
map_reduce_chain = MapReduceDocumentsChain(
llm_chain=map_chain,
reduce_documents_chain=reduce_documents_chain,
document_variable_name="docs",
return_intermediate_steps=False,
)
result = map_reduce_chain.invoke(chunks)['output_text']
print(types)
print(result)
if types != 'summ':
result = re.findall('(\{[^}]+\})', result)[0]
return eval(result)
return result
def get_entity_one(self, chunks):
result = self.llm.invoke(prompt_entity_one_chunk.format(chunks)).content
print('One')
print(result)
result = re.findall('(\{[^}]+\})', result)[0]
return eval(result)
def get_table(self, path):
start_time = datetime.now()
images = convert_from_path(path)
print('PDF to Image', round((datetime.now().timestamp() - start_time.timestamp()) / 60, 2), "minutes")
tables = []
# Loop pages
for image in images:
pixel_values = detection_transform(image).unsqueeze(0).to(device)
with torch.no_grad():
outputs = model(pixel_values)
id2label = model.config.id2label
id2label[len(model.config.id2label)] = "no object"
detected_tables = outputs_to_objects(outputs, image.size, id2label)
# Loop table in page (if any)
for idx in range(len(detected_tables)):
cropped_table = image.crop(detected_tables[idx]["bbox"])
if detected_tables[idx]["label"] == 'table rotated':
cropped_table = cropped_table.rotate(270, expand=True)
# TODO: what is the perfect threshold?
if detected_tables[idx]['score'] > 0.9:
print(detected_tables[idx])
tables.append(cropped_table)
print('Detect table from image', round((datetime.now().timestamp() - start_time.timestamp()) / 60, 2), "minutes")
genes = []
snps = []
diseases = []
# Loop tables
for table in tables:
buffer = io.BytesIO()
table.save(buffer, format='PNG')
image = Image(buffer)
# Extract to dataframe
extracted_tables = image.extract_tables(ocr=ocr, implicit_rows=True, borderless_tables=True, min_confidence=0)
if len(extracted_tables) == 0:
continue
# Combine multiple dataframe
df_table = extracted_tables[0].df
for extracted_table in extracted_tables[1:]:
df_table = pd.concat([df_table, extracted_table.df]).reset_index(drop=True)
df_table.loc[0] = df_table.loc[0].fillna('')
# Identify multiple rows (in dataframe) as one row (in image)
rows = []
indexes = []
for i in df_table.index:
if not df_table.loc[i].isna().any():
if len(indexes) > 0:
rows.append(indexes)
indexes = []
indexes.append(i)
rows.append(indexes)
df_table_cleaned = pd.DataFrame(columns=df_table.columns)
for row in rows:
row_str = df_table.loc[row[0]]
for idx in row[1:]:
row_str += ' ' + df_table.loc[idx].fillna('')
row_str = row_str.str.strip()
df_table_cleaned.loc[len(df_table_cleaned)] = row_str
# Ask LLM with JSON data
json_table = df_table_cleaned.to_json(orient='records')
str_json_table = json.dumps(json.loads(json_table), indent=2)
result = self.llm.invoke(prompt_table.format(str_json_table)).content
print('table')
print(result)
result = result[result.find('['):result.rfind(']')+1]
try:
result = eval(result)
except SyntaxError:
result = []
for res in result:
res_gene = res['Genes']
res_snp = res['SNPs']
res_disease = res['Diseases']
for snp in res_snp:
genes.append(res_gene)
snps.append(snp)
diseases.append(res_disease)
print('OCR table to extract', round((datetime.now().timestamp() - start_time.timestamp()) / 60, 2), "minutes")
print(genes, snps, diseases)
return genes, snps, diseases
def validate(self, df):
df = df.fillna('')
df['Genes'] = df['Genes'].str.replace(' ', '').str.upper()
df['SNPs'] = df['SNPs'].str.lower()
# Check if there is two gene names
sym = [',', '/', '|']
for i in df.index:
gene = df.loc[i, 'Genes']
for s in sym:
if s in gene:
genes = gene.split(s)
df.loc[i + 0.5] = df.loc[i]
df = df.sort_index().reset_index(drop=True)
df.loc[i, 'Genes'], df.loc[i + 1, 'Genes'] = genes[0], s.join(genes[1:])
break
# Check if there is SNPs without 'rs'
for i in df.index:
safe = True
snp = df.loc[i, 'SNPs']
snp = snp.replace('l', '1')
if re.fullmatch('rs(\d)+|', snp):
pass
elif re.fullmatch('ts(\d)+', snp):
snp = 'r' + snp[1:]
elif re.fullmatch('s(\d)+', snp):
snp = 'r' + snp
elif re.fullmatch('(\d)+', snp):
snp = 'rs' + snp
else:
safe = False
df = df.drop(i)
if safe:
df.loc[i, 'SNPs'] = snp
df.reset_index(drop=True, inplace=True)
df_clean = df.copy()
# # Validate genes and SNPs with APIs
def permutate(word):
if len(word) == 0:
return ['']
change = []
res = permutate(word[1:])
if word[0] in mistakes:
change = [mistakes[word[0]] + r for r in res]
return [word[0] + r for r in res] + change
def call(url):
while True:
try:
res = requests.get(url)
time.sleep(1)
break
except Exception as e:
print(e)
return res
mistakes = {'I': '1', 'O': '0'} # Common mistakes need to be maintained
dbsnp = {}
for i in df.index:
snp = df.loc[i, 'SNPs']
gene = df.loc[i, 'Genes']
if snp not in dbsnp:
res = call(f'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{snp}/')
try:
res = res.json()
dbsnp[snp] = [r['gene']['geneName'] for r in res['genomicContexts']]
except:
dbsnp[snp] = []
res = call(f'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=snp&retmode=json&id={snp[2:]}').json()['result'][snp[2:]]
if 'error' not in res:
dbsnp[snp].extend([r['name'] for r in res['genes']])
dbsnp[snp] = list(set(dbsnp[snp]))
if gene not in dbsnp[snp]:
for other in permutate(gene):
if other in dbsnp[snp]:
df.loc[i, 'Genes'] = other
print(f'{gene} corrected to {other}')
break
else:
df = df.drop(i)
# df.reset_index(drop=True, inplace=True)
df_no_llm = df.copy()
# Validate genes and diseases with LLM (for each 50 rows)
idx = 0
results = []
while True:
json_table = df[['Genes', 'SNPs', 'Diseases']][idx:idx+50].to_json(orient='records')
str_json_table = json.dumps(json.loads(json_table), indent=2)
result = self.llm_val.invoke(input=prompt_validation.format(str_json_table)).content
print('val', idx)
print(result)
result = result[result.find('['):result.rfind(']')+1]
try:
result = eval(result)
except SyntaxError:
result = []
results.extend(result)
idx += 50
if idx not in df.index:
break
df = pd.DataFrame(results)
df = df.merge(df_no_llm.head(1).drop(['Genes', 'SNPs', 'Diseases'], axis=1), 'cross')
return df, df_no_llm, df_clean |