Update icd9_ui.py

icd9_ui.py

@@ -1,147 +1,428 @@
-# # import streamlit as st
-# # import torch
-# # from transformers import LongformerTokenizer, LongformerForSequenceClassification
+import streamlit as st
+import torch
+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 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
 
-# # # 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'
-# # ]
+# 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
-# # def predict_icd9(texts, tokenizer, model, threshold=0.5):
-# #     inputs = tokenizer(
-# #         texts,
-# #         padding="max_length",
-# #         truncation=True,
-# #         max_length=512,
-# #         return_tensors="pt"
-# #     )
+# Function for making predictions
+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()
+    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)
+    predicted_icd9 = []
+    for pred in predictions:
+        codes = [icd9_columns[i] for i, val in enumerate(pred) if val == 1]
+        predicted_icd9.append(codes)
     
-# #     return predicted_icd9
+    return predicted_icd9
 
-# # # Streamlit UI
-# # st.title("ICD-9 Code Prediction")
-# # st.sidebar.header("Model Options")
-# # model_option = st.sidebar.selectbox("Select Model", [ "ClinicalLongformer"])
-# # 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 Codes")
-# #         for code in predictions[0]:
-# #             st.write(f"- {code}")
-# #     else:
-# #         st.error("Please enter a medical summary.")
+# Streamlit UI
+st.title("ICD-9 Code Prediction")
+st.sidebar.header("Model Options")
+model_option = st.sidebar.selectbox("Select Model", [ "ClinicalLongformer"])
+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 Codes")
+        for code in predictions[0]:
+            st.write(f"- {code}")
+    else:
+        st.error("Please enter a medical summary.")
 
-# # import torch
-# # import pandas as pd
-# # import streamlit as st
-# # from transformers import LongformerTokenizer, LongformerForSequenceClassification
+# 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 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 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
+# # 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 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 = [
-# #     '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'
-# # ]
+# # 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
-# # def predict_icd9(texts, tokenizer, model, threshold=0.5):
-# #     inputs = tokenizer(
-# #         texts,
-# #         padding="max_length",
-# #         truncation=True,
-# #         max_length=512,
-# #         return_tensors="pt"
-# #     )
+# # Function for making predictions
+# 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()
+#     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)
+#         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 for the predicted ICD-9 codes from the pre-loaded descriptions
-# #     predictions_with_desc = []
-# #     for codes in predicted_icd9:
-# #         code_with_desc = [(code, icd9_descriptions.get(code.replace('.', ''), "Description not found")) for code in codes]
-# #         predictions_with_desc.append(code_with_desc)
+#     # Fetch descriptions for the predicted ICD-9 codes from the pre-loaded descriptions
+#     predictions_with_desc = []
+#     for codes in predicted_icd9:
+#         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
+#     return predictions_with_desc
+
+
+# st.title("ICD-9 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 Codes and Descriptions")
+#         for code, description in predictions[0]:
+#             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 ICD-9 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 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 = [
+# # #     '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 ICD-9 predictions
+# # # 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)
+    
+# #     # predictions_with_desc = []
+# #     # for codes in predicted_icd9:
+# #     #     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 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 Codes and Descriptions")
-# #         for code, description in predictions[0]:
-# #             st.write(f"- {code}: {description}")
-# #     else:
-# #         st.error("Please enter a medical summary.")
+# # # 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:
+# # #         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")
+
+# # 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
+# # # Sidebar for Google API Key input
+# # st.sidebar.title("Settings")
+# # st.sidebar.write("Enter your Google API Key below for the Medical Image Analysis feature.")
+# # api_key = st.sidebar.text_input("Google API Key", type="password")
+
+# # if api_key:
+# #     st.session_state["GOOGLE_API_KEY"] = api_key
+# # else:
+# #     st.session_state.pop("GOOGLE_API_KEY", None)
+
+# # # 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 +430,8 @@
 
 # # # 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 +443,7 @@
 # #     '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 +452,7 @@
 # #         max_length=512,
 # #         return_tensors="pt"
 # #     )
-
+    
 # #     with torch.no_grad():
 # #         outputs = model(
 # #             input_ids=inputs["input_ids"],
@@ -189,105 +461,20 @@
 # #         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}")
-# #     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 ICD-9 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 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 = [
-# #     '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 ICD-9 predictions
-# # 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)
-    
-#     # predictions_with_desc = []
-#     # for codes in predicted_icd9:
-#     #     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("Medical Diagnosis Assistant")
 # # option = st.selectbox(
 # #     "Choose Diagnosis Method",
@@ -310,14 +497,14 @@
 # #         else:
 # #             st.error("Please enter a medical summary.")
 
-# # Medical Image Analysis
+# # # 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:
 # #         medical_agent = Agent(
 # #             model=Gemini(
-# #                 api_key=st.session_state.GOOGLE_API_KEY,
+# #                 api_key=st.session_state["GOOGLE_API_KEY"],
 # #                 id="gemini-2.0-flash-exp"
 # #             ),
 # #             tools=[DuckDuckGo()],
@@ -326,81 +513,76 @@
 
 # #         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))
+# #         - 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)
+# #                     st.image(resized_image, caption="Uploaded Medical Image", use_container_width=True)
                     
-#         #             analyze_button = st.button("🔍 Analyze Image")
+# #                     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 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")
+# #                     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")
 
 # import os
 # import torch
@@ -412,16 +594,6 @@
 # from phi.model.google import Gemini
 # from phi.tools.duckduckgo import DuckDuckGo
 
-# # Sidebar for Google API Key input
-# st.sidebar.title("Settings")
-# st.sidebar.write("Enter your Google API Key below for the Medical Image Analysis feature.")
-# api_key = st.sidebar.text_input("Google API Key", type="password")
-
-# if api_key:
-#     st.session_state["GOOGLE_API_KEY"] = api_key
-# else:
-#     st.session_state.pop("GOOGLE_API_KEY", None)
-
 # # Load the fine-tuned ICD-9 model and tokenizer
 # model_path = "./clinical_longformer"
 # tokenizer = LongformerTokenizer.from_pretrained(model_path)
@@ -429,7 +601,7 @@
 # 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 = pd.read_csv("D_ICD_DIAGNOSES.csv")
 # 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
 
@@ -452,7 +624,6 @@
 #         max_length=512,
 #         return_tensors="pt"
 #     )
-    
 #     with torch.no_grad():
 #         outputs = model(
 #             input_ids=inputs["input_ids"],
@@ -474,6 +645,9 @@
     
 #     return predictions_with_desc
 
+# # Define the API key directly in the code
+# GOOGLE_API_KEY = "AIzaSyA24A6egT3L0NAKkkw9QHjfoizp7cJUTaA"
+
 # # Streamlit UI
 # st.title("Medical Diagnosis Assistant")
 # option = st.selectbox(
@@ -499,260 +673,86 @@
 
 # # 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:
-#         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")
-
-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 ICD-9 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")
-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 = [
-    '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 ICD-9 predictions
-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)
-    
-    predictions_with_desc = []
-    for codes in predicted_icd9:
-        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
-
-# Define the API key directly in the code
-GOOGLE_API_KEY = "AIzaSyA24A6egT3L0NAKkkw9QHjfoizp7cJUTaA"
-
-# Streamlit UI
-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":
-    medical_agent = Agent(
-        model=Gemini(
-            api_key=GOOGLE_API_KEY,
-            id="gemini-2.0-flash-exp"
-        ),
-        tools=[DuckDuckGo()],
-        markdown=True
-    )
+#     medical_agent = Agent(
+#         model=Gemini(
+#             api_key=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"
-        )
+#     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))
+#     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)
+#                 st.image(resized_image, caption="Uploaded Medical Image", use_container_width=True)
                 
-                analyze_button = st.button("🔍 Analyze Image")
+#                 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 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")
+#                 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")