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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_max_length= 1024)
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() |