first app

biogpt_qa_pubmedqa_biogpt_large.py

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+# -*- coding: utf-8 -*-
+"""BioGPT-QA-PubMedQA-BioGPT-Large.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1grmKyUETABgEsC3hO7CKgnSG1JGBbmHV
+"""
+
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+import torch
+import re
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large-PubMedQA")
+model = AutoModelForCausalLM.from_pretrained("microsoft/BioGPT-Large-PubMedQA")
+
+# Check if GPU is available and move model to GPU
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+    model.to(device)
+    print("Using GPU:", torch.cuda.get_device_name(0))
+else:
+    device = torch.device("cpu")
+    print("Using CPU")
+
+def answer_bio_question(question, context):
+  """Answers a biological question using BioGPT-QA-PubMedQA-BioGPT-Large.
+
+  Args:
+      question: The question to be answered.
+      context: The context or passage containing the answer (concatenated with the question).
+
+  Returns:
+      The answer extracted from the context based on the question.
+  """
+  try:
+    # Concatenate question and context with a separator
+    input_ids = tokenizer(question + " [SEP] " + context, return_tensors="pt").to(device)
+
+    # Move the input to the chosen device (GPU or CPU)
+    input_ids = input_ids.to(device)
+
+    outputs = model.generate(**input_ids,
+                                    max_new_tokens=1024
+                             ,
+                                    num_beams=1,
+                                    early_stopping=False,
+                                    do_sample=False,
+                                    )
+
+
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    print(answer)
+    segs = re.search(r"the answer to the question given the context is(.*)", answer)
+    ans = "unknown"
+    if segs is not None:
+        segs = segs.groups()
+        ans = segs[0].strip()
+
+    return "The answer to this question is " + ans
+  except Exception as e:
+    print(f"Error during question answering: {e}")
+    return "An error occurred during question answering. Please try again."
+
+
+
+iface = gr.Interface(
+    fn=answer_bio_question,
+    inputs=[
+        gr.Textbox(label="Question", lines=2),
+        gr.Textbox(label="Context (Passage)", lines=5),
+    ],
+    outputs="textbox",
+    title="BioGPT-QA: Answer Biological Questions",
+)
+
+# Launch the Gradio interface
+iface.launch( debug=True, share=True)
+

requirements.txt

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+transformers
+torch
+gradio
+sacremoses