Update icd9_ui.py

icd9_ui.py

@@ -136,12 +136,106 @@
 #             st.write(f"- {code}: {description}")
 #     else:
 #         st.error("Please enter a medical summary.")
+# import torch
+# import pandas as pd
+# import streamlit as st
+# from transformers import LongformerTokenizer, LongformerForSequenceClassification
+
+# # Load the fine-tuned model and tokenizer
+# model_path = "./clinical_longformer"
+# tokenizer = LongformerTokenizer.from_pretrained(model_path)
+# model = LongformerForSequenceClassification.from_pretrained(model_path)
+# model.eval()  # Set the model to evaluation mode
+
+# # Load the ICD-9 descriptions from CSV into a dictionary
+# icd9_desc_df = pd.read_csv("D_ICD_DIAGNOSES.csv")  # Adjust the path to your CSV file
+# icd9_desc_df['ICD9_CODE'] = icd9_desc_df['ICD9_CODE'].astype(str)  # Ensure ICD9_CODE is string type
+# icd9_descriptions = dict(zip(icd9_desc_df['ICD9_CODE'].str.replace('.', ''), icd9_desc_df['LONG_TITLE']))  # Remove decimals for matching
+
+# # Load the ICD-9 to ICD-10 mapping
+# icd9_to_icd10 = {}
+# with open("2015_I9gem.txt", "r") as file:
+#     for line in file:
+#         parts = line.strip().split()
+#         if len(parts) == 3:
+#             icd9, icd10, _ = parts
+#             icd9_to_icd10[icd9] = icd10
+
+# # ICD-9 code columns used during training
+# icd9_columns = [
+#     '038.9', '244.9', '250.00', '272.0', '272.4', '276.1', '276.2', '285.1', '285.9',
+#     '287.5', '305.1', '311', '36.15', '37.22', '37.23', '38.91', '38.93', '39.61',
+#     '39.95', '401.9', '403.90', '410.71', '412', '414.01', '424.0', '427.31', '428.0',
+#     '486', '496', '507.0', '511.9', '518.81', '530.81', '584.9', '585.9', '599.0',
+#     '88.56', '88.72', '93.90', '96.04', '96.6', '96.71', '96.72', '99.04', '99.15',
+#     '995.92', 'V15.82', 'V45.81', 'V45.82', 'V58.61'
+# ]
+
+# # Function for making predictions and mapping to ICD-10
+# def predict_icd9(texts, tokenizer, model, threshold=0.5):
+#     inputs = tokenizer(
+#         texts,
+#         padding="max_length",
+#         truncation=True,
+#         max_length=512,
+#         return_tensors="pt"
+#     )
+
+#     with torch.no_grad():
+#         outputs = model(
+#             input_ids=inputs["input_ids"],
+#             attention_mask=inputs["attention_mask"]
+#         )
+#         logits = outputs.logits
+#         probabilities = torch.sigmoid(logits)
+#         predictions = (probabilities > threshold).int()
+
+#     predicted_icd9 = []
+#     for pred in predictions:
+#         codes = [icd9_columns[i] for i, val in enumerate(pred) if val == 1]
+#         predicted_icd9.append(codes)
+
+#     # Fetch descriptions and map to ICD-10 codes
+#     predictions_with_desc = []
+#     for codes in predicted_icd9:
+#         code_with_desc = []
+#         for code in codes:
+#             icd9_stripped = code.replace('.', '')
+#             icd10_code = icd9_to_icd10.get(icd9_stripped, "Mapping not found")
+#             icd9_desc = icd9_descriptions.get(icd9_stripped, "Description not found")
+#             code_with_desc.append((code, icd9_desc, icd10_code))
+#         predictions_with_desc.append(code_with_desc)
+
+#     return predictions_with_desc
+
+# # Streamlit UI
+# st.title("ICD-9 to ICD-10 Code Prediction")
+# st.sidebar.header("Model Options")
+# threshold = st.sidebar.slider("Prediction Threshold", 0.0, 1.0, 0.5, 0.01)
+
+# st.write("### Enter Medical Summary")
+# input_text = st.text_area("Medical Summary", placeholder="Enter clinical notes here...")
+
+# if st.button("Predict"):
+#     if input_text.strip():
+#         predictions = predict_icd9([input_text], tokenizer, model, threshold)
+#         st.write("### Predicted ICD-9 and ICD-10 Codes with Descriptions")
+#         for icd9_code, description, icd10_code in predictions[0]:
+#             st.write(f"- ICD-9: {icd9_code} ({description}) -> ICD-10: {icd10_code}")
+#     else:
+#         st.error("Please enter a medical summary.")
+
+import os
 import torch
 import pandas as pd
 import streamlit as st
+from PIL import Image
 from transformers import LongformerTokenizer, LongformerForSequenceClassification
+from phi.agent import Agent
+from phi.model.google import Gemini
+from phi.tools.duckduckgo import DuckDuckGo
 
-# Load the fine-tuned model and tokenizer
+# Load the fine-tuned ICD-9 model and tokenizer
 model_path = "./clinical_longformer"
 tokenizer = LongformerTokenizer.from_pretrained(model_path)
 model = LongformerForSequenceClassification.from_pretrained(model_path)
@@ -149,17 +243,8 @@ model.eval()  # Set the model to evaluation mode
 
 # Load the ICD-9 descriptions from CSV into a dictionary
 icd9_desc_df = pd.read_csv("D_ICD_DIAGNOSES.csv")  # Adjust the path to your CSV file
-icd9_desc_df['ICD9_CODE'] = icd9_desc_df['ICD9_CODE'].astype(str)  # Ensure ICD9_CODE is string type
-icd9_descriptions = dict(zip(icd9_desc_df['ICD9_CODE'].str.replace('.', ''), icd9_desc_df['LONG_TITLE']))  # Remove decimals for matching
-
-# Load the ICD-9 to ICD-10 mapping
-icd9_to_icd10 = {}
-with open("2015_I9gem.txt", "r") as file:
-    for line in file:
-        parts = line.strip().split()
-        if len(parts) == 3:
-            icd9, icd10, _ = parts
-            icd9_to_icd10[icd9] = icd10
+icd9_desc_df['ICD9_CODE'] = icd9_desc_df['ICD9_CODE'].astype(str)  # Ensure ICD9_CODE is string type for matching
+icd9_descriptions = dict(zip(icd9_desc_df['ICD9_CODE'].str.replace('.', ''), icd9_desc_df['LONG_TITLE']))  # Remove decimals in ICD9 code for matching
 
 # ICD-9 code columns used during training
 icd9_columns = [
@@ -171,7 +256,7 @@ icd9_columns = [
     '995.92', 'V15.82', 'V45.81', 'V45.82', 'V58.61'
 ]
 
-# Function for making predictions and mapping to ICD-10
+# Function for making ICD-9 predictions
 def predict_icd9(texts, tokenizer, model, threshold=0.5):
     inputs = tokenizer(
         texts,
@@ -180,7 +265,7 @@ def predict_icd9(texts, tokenizer, model, threshold=0.5):
         max_length=512,
         return_tensors="pt"
     )
-
+    
     with torch.no_grad():
         outputs = model(
             input_ids=inputs["input_ids"],
@@ -189,40 +274,132 @@ def predict_icd9(texts, tokenizer, model, threshold=0.5):
         logits = outputs.logits
         probabilities = torch.sigmoid(logits)
         predictions = (probabilities > threshold).int()
-
+    
     predicted_icd9 = []
     for pred in predictions:
         codes = [icd9_columns[i] for i, val in enumerate(pred) if val == 1]
         predicted_icd9.append(codes)
-
-    # Fetch descriptions and map to ICD-10 codes
+    
     predictions_with_desc = []
     for codes in predicted_icd9:
-        code_with_desc = []
-        for code in codes:
-            icd9_stripped = code.replace('.', '')
-            icd10_code = icd9_to_icd10.get(icd9_stripped, "Mapping not found")
-            icd9_desc = icd9_descriptions.get(icd9_stripped, "Description not found")
-            code_with_desc.append((code, icd9_desc, icd10_code))
+        code_with_desc = [(code, icd9_descriptions.get(code.replace('.', ''), "Description not found")) for code in codes]
         predictions_with_desc.append(code_with_desc)
-
+    
     return predictions_with_desc
 
 # Streamlit UI
-st.title("ICD-9 to ICD-10 Code Prediction")
-st.sidebar.header("Model Options")
-threshold = st.sidebar.slider("Prediction Threshold", 0.0, 1.0, 0.5, 0.01)
-
-st.write("### Enter Medical Summary")
-input_text = st.text_area("Medical Summary", placeholder="Enter clinical notes here...")
-
-if st.button("Predict"):
-    if input_text.strip():
-        predictions = predict_icd9([input_text], tokenizer, model, threshold)
-        st.write("### Predicted ICD-9 and ICD-10 Codes with Descriptions")
-        for icd9_code, description, icd10_code in predictions[0]:
-            st.write(f"- ICD-9: {icd9_code} ({description}) -> ICD-10: {icd10_code}")
+st.title("Medical Diagnosis Assistant")
+option = st.selectbox(
+    "Choose Diagnosis Method",
+    ("ICD-9 Code Prediction", "Medical Image Analysis")
+)
+
+# ICD-9 Code Prediction
+if option == "ICD-9 Code Prediction":
+    st.write("### Enter Medical Summary")
+    input_text = st.text_area("Medical Summary", placeholder="Enter clinical notes here...")
+
+    threshold = st.slider("Prediction Threshold", 0.0, 1.0, 0.5, 0.01)
+
+    if st.button("Predict ICD-9 Codes"):
+        if input_text.strip():
+            predictions = predict_icd9([input_text], tokenizer, model, threshold)
+            st.write("### Predicted ICD-9 Codes and Descriptions")
+            for code, description in predictions[0]:
+                st.write(f"- {code}: {description}")
+        else:
+            st.error("Please enter a medical summary.")
+
+# Medical Image Analysis
+elif option == "Medical Image Analysis":
+    if "GOOGLE_API_KEY" not in st.session_state:
+        st.warning("Please enter your Google API Key in the sidebar to continue")
     else:
-        st.error("Please enter a medical summary.")
+        medical_agent = Agent(
+            model=Gemini(
+                api_key=st.session_state.GOOGLE_API_KEY,
+                id="gemini-2.0-flash-exp"
+            ),
+            tools=[DuckDuckGo()],
+            markdown=True
+        )
+
+        query = """
+        You are a highly skilled medical imaging expert with extensive knowledge in radiology and diagnostic imaging. Analyze the patient's medical image and structure your response as follows:
+
+        ### 1. Image Type & Region
+        - Specify imaging modality (X-ray/MRI/CT/Ultrasound/etc.)
+        - Identify the patient's anatomical region and positioning
+        - Comment on image quality and technical adequacy
+
+        ### 2. Key Findings
+        - List primary observations systematically
+        - Note any abnormalities in the patient's imaging with precise descriptions
+        - Include measurements and densities where relevant
+        - Describe location, size, shape, and characteristics
+        - Rate severity: Normal/Mild/Moderate/Severe
+
+        ### 3. Diagnostic Assessment
+        - Provide primary diagnosis with confidence level
+        - List differential diagnoses in order of likelihood
+        - Support each diagnosis with observed evidence from the patient's imaging
+        - Note any critical or urgent findings
+
+        ### 4. Patient-Friendly Explanation
+        - Explain the findings in simple, clear language that the patient can understand
+        - Avoid medical jargon or provide clear definitions
+        - Include visual analogies if helpful
+        - Address common patient concerns related to these findings
+
+        ### 5. Research Context
+        - Use the DuckDuckGo search tool to find recent medical literature about similar cases
+        - Provide a list of relevant medical links
+        - Include key references to support your analysis
+        """
+
+        upload_container = st.container()
+        image_container = st.container()
+        analysis_container = st.container()
+
+        with upload_container:
+            uploaded_file = st.file_uploader(
+                "Upload Medical Image",
+                type=["jpg", "jpeg", "png", "dicom"],
+                help="Supported formats: JPG, JPEG, PNG, DICOM"
+            )
+
+        if uploaded_file is not None:
+            with image_container:
+                col1, col2, col3 = st.columns([1, 2, 1])
+                with col2:
+                    image = Image.open(uploaded_file)
+                    width, height = image.size
+                    aspect_ratio = width / height
+                    new_width = 500
+                    new_height = int(new_width / aspect_ratio)
+                    resized_image = image.resize((new_width, new_height))
+                    
+                    st.image(resized_image, caption="Uploaded Medical Image", use_container_width=True)
+                    
+                    analyze_button = st.button("🔍 Analyze Image")
+
+            with analysis_container:
+                if analyze_button:
+                    image_path = "temp_medical_image.png"
+                    with open(image_path, "wb") as f:
+                        f.write(uploaded_file.getbuffer())
+                    
+                    with st.spinner("🔄 Analyzing image... Please wait."):
+                        try:
+                            response = medical_agent.run(query, images=[image_path])
+                            st.markdown("### 📋 Analysis Results")
+                            st.markdown(response.content)
+                        except Exception as e:
+                            st.error(f"Analysis error: {e}")
+                        finally:
+                            if os.path.exists(image_path):
+                                os.remove(image_path)
+        else:
+            st.info("👆 Please upload a medical image to begin analysis")