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import gradio as gr | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
import torch | |
import requests | |
from bs4 import BeautifulSoup | |
import time | |
import json | |
import xml.etree.ElementTree as ET | |
# Move models to CUDA if available | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
tokenizer = AutoTokenizer.from_pretrained("stanford-crfm/BioMedLM") | |
model = AutoModelForCausalLM.from_pretrained("stanford-crfm/BioMedLM").to(device) | |
api_key = '2c78468d6246082d456a140bb1de415ed108' | |
num_results = 10 | |
def extract_longer_answers_from_paragraphs(paragraphs, query, tokenizer, model): | |
context = " ".join(paragraphs) | |
question = f"What is the mechanism of {query}?" | |
context += question | |
inputs = tokenizer(context, return_tensors="pt", add_special_tokens=False, output_attentions=False).to(device) | |
top_p = 0.9 # Adjust as needed | |
max_len = 50 # Adjust as needed | |
outputs = model.generate( | |
**inputs, | |
top_p=top_p, | |
max_length=max_len, | |
num_beams=1, # Adjust as needed | |
no_repeat_ngram_size=2 # Adjust as needed | |
) | |
answer = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return answer | |
def retrieve_and_answer(query1, query2): | |
combined_query1 = f"({query1}) AND ({query2})" | |
answer = fetch_and_generate(query1, combined_query, tokenizer, model) | |
return answer1, answer2 | |
def fetch_and_generate(query, combined_query, tokenizer, model): | |
esearch_url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&api_key={api_key}&term={combined_query}&retmax={num_results}&sort=relevance" | |
headers = {'Accept': 'application/json'} | |
response = requests.get(esearch_url, headers=headers) | |
root = ET.fromstring(response.text) | |
if response.status_code == 200: | |
paragraphs = [] | |
for article_id in root.find('IdList'): | |
article_id = article_id.text | |
efetch_url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&api_key={api_key}&id={article_id}&retmode=xml" | |
response = requests.get(efetch_url) | |
if response.status_code == 200: | |
article_data = response.text | |
soup = BeautifulSoup(article_data, 'xml') | |
articles = soup.find_all('PubmedArticle') | |
for article in articles: | |
title = article.find('ArticleTitle') | |
if title: | |
title_text = title.text | |
if article.find('AbstractText'): | |
paragraphs.append(article.find('AbstractText').text) | |
else: | |
print("Error:", response.status_code) | |
time.sleep(3) | |
answer = extract_longer_answers_from_paragraphs(paragraphs, query, tokenizer, model) | |
return answer | |
else: | |
print("Error:", response.status_code) | |
return "Error fetching articles.", [] | |
# Gradio Interface | |
iface = gr.Interface( | |
fn=retrieve_and_answer, | |
inputs=[gr.Textbox(placeholder="Enter Query 1", label= 'query1'), | |
gr.Textbox(placeholder="Enter Query 2", label= 'query2')], | |
outputs=[ gr.Textbox(placeholder="Answer from BioMedLM"), ], | |
live=True, | |
title="PubMed Question Answering: Stanford/BioMedLM", | |
description="Enter two queries to retrieve PubMed articles", | |
examples=[ | |
["sertraline", "mechanism"], | |
["cancer", "treatment"] | |
] | |
) | |
iface.launch() |