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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, force_download= True) | |
| model = AutoModelForCausalLM.from_pretrained("stanford-crfm/BioMedLM", force_download= True).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() |