Update app.py

app.py

@@ -1,120 +1,120 @@
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import MultiLabelBinarizer
-
-# Check if a GPU is available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load the trained model and tokenizer
-@st.cache_resource
-def load_model():
-    model = AutoModelForSequenceClassification.from_pretrained(
-        "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract",
-        num_labels=8,  # Adjust based on your label count
-        problem_type="multi_label_classification"
-    )
-    model.load_state_dict(torch.load('best_model_v2.pth'))
-    model.eval()
-    tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract")
-    model = model.to(device)  # Move the model to the correct device
-
-    return model, tokenizer
-
-@st.cache_resource
-def load_mlb():
-    # Define the classes based on your label set
-    # classes = ['E11.9', 'I10', 'J45.909', 'M54.5', 'N39.0', '81001.0', '99213.0', '99214.0']
-    classes = ['81001.0','99213.0','99214.0','E11.9','I10','J45.909','M54.5','N39.0']
-    # Initialize and fit the MultiLabelBinarizer
-    mlb = MultiLabelBinarizer(classes=classes)
-    mlb.fit([classes])  # Fit with the full list of labels as a single sample
-    
-    return mlb
-
-
-# # Load MultiLabelBinarizer
-# @st.cache_resource
-# def load_mlb():
-#     mlb = MultiLabelBinarizer()
-#     # mlb.classes_ = np.load('mlb_classes.npy')  # Assuming you saved the classes array during training
-#     mlb = MultiLabelBinarizer(classes=['E11.9', 'I10', 'J45.909', 'M54.5',
-#        'N39.0', '81001.0', '99213.0', '99214.0'])  # Update with actual labels
-
-#     return mlb
-
-model, tokenizer = load_model()
-mlb = load_mlb()
-
-# Streamlit UI
-st.title("Automated Coding and Billing Prediction")
-# st.write("Enter clinical notes to predict ICD and CPT codes.")
-
-# Text input for Clinical Notes
-clinical_note = st.text_area("Enter clinical notes to predict ICD and CPT codes")
-
-# Prediction button
-if st.button('Predict'):
-    if clinical_note:
-        # Tokenize the input clinical note
-        inputs = tokenizer(clinical_note, truncation=True, padding="max_length", max_length=512, return_tensors='pt')
-        
-        # Move inputs to the GPU if available
-        inputs = {key: val.to(device) for key, val in inputs.items()}
-        
-        # Model inference
-        with torch.no_grad():
-            outputs = model(**inputs)
-            logits = outputs.logits
-        
-        # Apply sigmoid and threshold the output (0.5 for multi-label classification)
-        pred_labels = (torch.sigmoid(logits) > 0.5).cpu().numpy()
-        
-        # Get the predicted ICD and CPT codes
-        predicted_codes = mlb.inverse_transform(pred_labels)
-        
-        # Format the results for better display
-        if predicted_codes:
-            st.write("**Predicted ICD and CPT Codes:**")
-            for codes in predicted_codes:
-                for code in codes:
-                    if code in ['81001.0', '99213.0', '99214.0']:  # Adjust based on your CPT code list
-                        st.write(f"- **CPT Code:** {code}")
-                    else:
-                        st.write(f"- **ICD Code:** {code}")
-        else:
-            st.write("No codes predicted.")
-        
-    # else:
-    #     st.write("Please enter clinical notes for prediction.")
-
-
-# # Prediction button
-# if st.button('Predict'):
-#     if clinical_note:
-#         # Tokenize the input clinical note
-#         inputs = tokenizer(clinical_note, truncation=True, padding="max_length", max_length=512, return_tensors='pt')
-        
-#         # Move inputs to the GPU if available
-#         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-#         inputs = {key: val.to(device) for key, val in inputs.items()}
-        
-#         # Model inference
-#         with torch.no_grad():
-#             outputs = model(**inputs)
-#             logits = outputs.logits
-        
-#         # Apply sigmoid and threshold the output (0.5 for multi-label classification)
-#         pred_labels = (torch.sigmoid(logits) > 0.5).cpu().numpy()
-        
-#         # Get the predicted ICD and CPT codes
-#         predicted_codes = mlb.inverse_transform(pred_labels)
-        
-#         # Show the results
-#         st.write("Predicted ICD and CPT Codes:")
-#         st.write(predicted_codes)
-        
-#     else:
-#         st.write("Please enter clinical notes for prediction.")
+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import MultiLabelBinarizer
+
+# Check if a GPU is available
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the trained model and tokenizer
+@st.cache_resource
+def load_model():
+    model = AutoModelForSequenceClassification.from_pretrained(
+        "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract",
+        num_labels=8,  # Adjust based on your label count
+        problem_type="multi_label_classification"
+    )
+    model.load_state_dict(torch.load('best_model_v2.pth'))
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract")
+    model = model.to(device)  # Move the model to the correct device
+
+    return model, tokenizer
+
+@st.cache_resource
+def load_mlb():
+    # Define the classes based on your label set
+    # classes = ['E11.9', 'I10', 'J45.909', 'M54.5', 'N39.0', '81001.0', '99213.0', '99214.0']
+    classes = ['81001.0','99213.0','99214.0','E11.9','I10','J45.909','M54.5','N39.0']
+    # Initialize and fit the MultiLabelBinarizer
+    mlb = MultiLabelBinarizer(classes=classes)
+    mlb.fit([classes])  # Fit with the full list of labels as a single sample
+    
+    return mlb
+
+
+# # Load MultiLabelBinarizer
+# @st.cache_resource
+# def load_mlb():
+#     mlb = MultiLabelBinarizer()
+#     # mlb.classes_ = np.load('mlb_classes.npy')  # Assuming you saved the classes array during training
+#     mlb = MultiLabelBinarizer(classes=['E11.9', 'I10', 'J45.909', 'M54.5',
+#        'N39.0', '81001.0', '99213.0', '99214.0'])  # Update with actual labels
+
+#     return mlb
+
+model, tokenizer = load_model()
+mlb = load_mlb()
+
+# Streamlit UI
+st.title("Automated Coding and Billing Prediction")
+# st.write("Enter clinical notes to predict ICD and CPT codes.")
+
+# Text input for Clinical Notes
+clinical_note = st.text_area("Enter clinical notes to predict ICD and CPT codes")
+
+# Prediction button
+if st.button('Predict'):
+    if clinical_note:
+        # Tokenize the input clinical note
+        inputs = tokenizer(clinical_note, truncation=True, padding="max_length", max_length=512, return_tensors='pt')
+        
+        # Move inputs to the GPU if available
+        inputs = {key: val.to(device) for key, val in inputs.items()}
+        
+        # Model inference
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+        
+        # Apply sigmoid and threshold the output (0.5 for multi-label classification)
+        pred_labels = (torch.sigmoid(logits) > 0.5).cpu().numpy()
+        
+        # Get the predicted ICD and CPT codes
+        predicted_codes = mlb.inverse_transform(pred_labels)
+        
+        # Format the results for better display
+        if predicted_codes:
+            st.write("**Predicted ICD and CPT Codes:**")
+            for codes in predicted_codes:
+                for code in codes:
+                    if code in ['81001.0', '99213.0', '99214.0']:  # Adjust based on your CPT code list
+                        st.write(f"- **CPT Code:** {code}")
+                    else:
+                        st.write(f"- **ICD Code:** {code}")
+        else:
+            st.write("No codes predicted.")
+        
+    # else:
+    #     st.write("Please enter clinical notes for prediction.")
+
+
+# # Prediction button
+# if st.button('Predict'):
+#     if clinical_note:
+#         # Tokenize the input clinical note
+#         inputs = tokenizer(clinical_note, truncation=True, padding="max_length", max_length=512, return_tensors='pt')
+        
+#         # Move inputs to the GPU if available
+#         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#         inputs = {key: val.to(device) for key, val in inputs.items()}
+        
+#         # Model inference
+#         with torch.no_grad():
+#             outputs = model(**inputs)
+#             logits = outputs.logits
+        
+#         # Apply sigmoid and threshold the output (0.5 for multi-label classification)
+#         pred_labels = (torch.sigmoid(logits) > 0.5).cpu().numpy()
+        
+#         # Get the predicted ICD and CPT codes
+#         predicted_codes = mlb.inverse_transform(pred_labels)
+        
+#         # Show the results
+#         st.write("Predicted ICD and CPT Codes:")
+#         st.write(predicted_codes)
+        
+#     else:
+#         st.write("Please enter clinical notes for prediction.")