Initial commit

app.py

@@ -0,0 +1,46 @@
+model_path = 'elliealbertson/identifying_pregnancy_clinical_notes'
+tokenizer = BertTokenizer.from_pretrained(model_path)
+model = BertForSequenceClassification.from_pretrained(model_path)
+
+def predict(text):
+
+    inputs = tokenizer(text, return_tensors="pt")
+    num_tokens = inputs['input_ids'].size(1)
+
+    if num_tokens <= 512:
+
+        outputs = model(**inputs)
+
+        predicted_class_id = torch.argmax(outputs.logits).item()
+
+        probability_of_predicted_class = round(torch.nn.functional.softmax(outputs.logits, dim=1)[0, predicted_class_id].item(),2)
+
+        if (predicted_class_id == 0) & (probability_of_predicted_class >= 0.5):
+            predicted_class_label = "No, the note does not discuss the patient's pregnancy based on the model's assessment."
+        elif (predicted_class_id == 1) & (probability_of_predicted_class >= 0.5):
+            predicted_class_label = "Yes, the note discusses the patient's pregnancy based on the model's assessment."
+        else:
+            predicted_class_label = "The model was unable to determine with high certainty whether or not the note discusses the patient's pregnancy. Please provide additional text or a different note."
+        
+        return predicted_class_label
+    
+    else:
+        
+        error_message = 'Unfortunately the model is limited in how much text it can process at once. Please enter a shorter note.'
+
+        return error_message
+
+with gr.Blocks() as interface:
+    gr.Markdown("<h1 align='center'>Identifying Pregnancy in Clinical Notes</h1>")
+    gr.Markdown("<p align='center'>Use this app to classify a clinical note as discussing or not discussing the patient's pregnancy.</p>")
+    with gr.Row():
+        with gr.Column():
+            inputs = gr.Textbox(label='Input a clinical note here:', lines=4)
+            button = gr.Button('Assess Note')
+            gr.Examples(['The patient is pregnant.', 'She has high cholesterol and hypertension.', 'Normal vaginal delivery.', 'Fetus development normal.', 'Presented with nausea.', 'Broken arm and leg.'], inputs)
+        with gr.Column():
+            outputs=gr.Textbox(label="Does the note discuss the patient's pregnancy?", lines=4)
+            button.click(fn=predict, inputs=inputs, outputs=outputs, queue=False)
+    gr.Markdown("<p align='center'>Model fine-tuned from <a href='https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT' target='_blank'> Bio+ClinicalBERT </a>.</p>")
+
+interface.launch()