Commit
•
e9af536
1
Parent(s):
4fcfab4
init
Browse files- app.py +145 -0
- requirements.txt +7 -0
app.py
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import os
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import torch
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from datasets import load_dataset, DatasetDict
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from transformers import AutoTokenizer, AutoModel
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import chromadb
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import gradio as gr
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from sklearn.metrics import precision_score, recall_score, f1_score
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# Mean Pooling - Take attention mask into account for correct averaging
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def meanpooling(output, mask):
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embeddings = output[0] # First element of model_output contains all token embeddings
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mask = mask.unsqueeze(-1).expand(embeddings.size()).float()
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return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9)
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# Load the dataset
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dataset = load_dataset("thankrandomness/mimic-iii-sample")
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# Split the dataset into train and validation sets
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split_dataset = dataset['train'].train_test_split(test_size=0.2, seed=42)
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dataset = DatasetDict({
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'train': split_dataset['train'],
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'validation': split_dataset['test']
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})
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# Load the model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("neuml/pubmedbert-base-embeddings-matryoshka")
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model = AutoModel.from_pretrained("neuml/pubmedbert-base-embeddings-matryoshka")
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# Function to embed text using mean pooling
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def embed_text(text):
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inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors='pt')
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with torch.no_grad():
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output = model(**inputs)
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embeddings = meanpooling(output, inputs['attention_mask'])
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return embeddings.numpy().tolist()
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# Initialize ChromaDB client
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client = chromadb.Client()
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collection = client.create_collection(name="pubmedbert_matryoshka_embeddings")
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# Function to upsert data into ChromaDB
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def upsert_data(dataset_split):
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for i, row in enumerate(dataset_split):
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for note in row['notes']:
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text = note.get('text', '')
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annotations_list = []
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for annotation in note.get('annotations', []):
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try:
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code = annotation['code']
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code_system = annotation['code_system']
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description = annotation['description']
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annotations_list.append({"code": code, "code_system": code_system, "description": description})
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except KeyError as e:
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print(f"Skipping annotation due to missing key: {e}")
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if text and annotations_list:
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embeddings = embed_text([text])[0]
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# Upsert data, embeddings, and annotations into ChromaDB
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for j, annotation in enumerate(annotations_list):
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collection.upsert(
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ids=[f"note_{note['note_id']}_{j}"],
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embeddings=[embeddings],
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metadatas=[annotation]
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)
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else:
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print(f"Skipping note {note['note_id']} due to missing 'text' or 'annotations'")
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# Upsert training data
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upsert_data(dataset['train'])
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# Define retrieval function
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def retrieve_relevant_text(input_text):
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input_embedding = embed_text([input_text])[0] # Get the embedding for the single input text
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results = collection.query(
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query_embeddings=[input_embedding],
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n_results=5,
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include=["metadatas", "documents", "distances"]
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)
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# Extract code and similarity scores
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output = []
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for metadata, distance in zip(results['metadatas'][0], results['distances'][0]):
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output.append({
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"similarity_score": distance,
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"code": metadata['code'],
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"code_system": metadata['code_system'],
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"description": metadata['description']
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})
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return output
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# Evaluate retrieval efficiency on the validation/test set
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def evaluate_efficiency(dataset_split):
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y_true = []
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y_pred = []
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for i, row in enumerate(dataset_split):
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for note in row['notes']:
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text = note.get('text', '')
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annotations_list = [annotation['code'] for annotation in note.get('annotations', []) if 'code' in annotation]
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if text and annotations_list:
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retrieved_results = retrieve_relevant_text(text)
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retrieved_codes = [result['code'] for result in retrieved_results]
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# Ground truth
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y_true.extend(annotations_list)
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# Predictions (limit to length of true annotations to avoid mismatch)
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y_pred.extend(retrieved_codes[:len(annotations_list)])
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if len(y_true) != len(y_pred):
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min_length = min(len(y_true), len(y_pred))
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y_true = y_true[:min_length]
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y_pred = y_pred[:min_length]
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precision = precision_score(y_true, y_pred, average='macro')
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recall = recall_score(y_true, y_pred, average='macro')
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f1 = f1_score(y_true, y_pred, average='macro')
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return precision, recall, f1
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# Calculate retrieval efficiency metrics
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precision, recall, f1 = evaluate_efficiency(dataset['validation'])
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# Gradio interface
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def gradio_interface(input_text):
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results = retrieve_relevant_text(input_text)
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formatted_results = [
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f"Similarity Score: {result['similarity_score']:.2f}, Code: {result['code']}, Description: {result['description']}"
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for result in results
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]
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return formatted_results
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# Display retrieval efficiency metrics
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metrics = f"Precision: {precision:.2f}, Recall: {recall:.2f}, F1 Score: {f1:.2f}"
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with gr.Blocks() as interface:
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gr.Markdown("# Text Retrieval with Efficiency Metrics")
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gr.Markdown(metrics)
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text_input = gr.Textbox(label="Input Text")
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text_output = gr.Textbox(label="Retrieved Results")
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submit_button = gr.Button("Submit")
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submit_button.click(fn=gradio_interface, inputs=text_input, outputs=text_output)
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interface.launch()
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requirements.txt
ADDED
@@ -0,0 +1,7 @@
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|
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1 |
+
torch
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2 |
+
transformers
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3 |
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datasets
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4 |
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chromadb
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5 |
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gradio
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numpy
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scikit-learn
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