Create app.py

app.py

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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 and compare answers from different models.",
+    examples=[
+        ["sertraline", "mechanism"],
+        ["cancer", "treatment"]
+    ]
+)
+
+iface.launch()