feat: add jsonification

app.py

@@ -4,9 +4,10 @@ from pathlib import Path
 import numpy as np
 import gradio as gr
 import requests
+import json
 
 # Store the server's URL
-SERVER_URL = "http://127.0.0.1:7860/"
+SERVER_URL = "https://ppaihack-match.azurewebsites.net/"
 CURRENT_DIR = Path(__file__).parent
 DEPLOYMENT_DIR = CURRENT_DIR / "deployment_files"
 KEYS_DIR = DEPLOYMENT_DIR / ".fhe_keys"
@@ -118,7 +119,7 @@ def decrypt_result(encrypted_answer: bytes, user_id: str) -> bool:
 
 
 def encode_categorical_data(data):
-    categories = ["Gender", "Ethnicity", "Geographic_Location", "Smoking_Status", "Alcohol_Consumption", "Exercise_Habits", "Diet", "Functional_Status", "Previous_Trial_Participation"]
+    categories = ["Gender", "Ethnicity", "Geographic_Location", "Diagnoses_ICD10", "Medications", "Allergies", "Previous_Treatments", "Smoking_Status", "Alcohol_Consumption", "Exercise_Habits", "Diet", "Functional_Status", "Previous_Trial_Participation"]
     encoded_data = []
     for i in range(len(categories)):
         sub_cats = additional_categories[categories[i]]
@@ -129,22 +130,69 @@ def encode_categorical_data(data):
         
     return encoded_data
 
+def clear_data_to_json(data):
+    print(data)
+    patient_data = {
+        "model_names": ["my_model"],
+        "patient": {
+            "Age": data.get("age", 30),
+            "Blood_Glucose_Level": data.get("blood_glucose_level", 0),
+            "Blood_Pressure_Systolic": data.get("blood_pressure_systolic", 0),
+            "Blood_Pressure_Diastolic": data.get("blood_pressure_diastolic", 0),
+            "BMI": data.get("bmi", 0),
+            "Condition_Severity": data.get("condition_severity", 0),
+            "Gender": data.get("Gender", 0),
+            "Ethnicity": data.get("Ethnicity", 0),
+            "Geographic_Location": data.get("Geographic_Location", 0),
+            "Smoking_Status": data.get("Smoking_Status", 0),
+            "Diagnoses_ICD10": data.get("Diagnoses_ICD10", 0),
+            "Medications": data.get("Medications", 0),
+            "Allergies": data.get("Allergies", 0),
+            "Previous_Treatments": data.get("Previous_Treatments", 0),
+            "Alcohol_Consumption": data.get("Alcohol_Consumption", 0),
+            "Exercise_Habits": data.get("Exercise_Habits", 0),
+            "Diet": data.get("Diet", 0),
+            "Functional_Status": data.get("Functional_Status", 0),
+            "Previous_Trial_Participation": data.get("Previous_Trial_Participation", 0)
+        }
+    }
+    return json.dumps(patient_data, indent=4)
+
 
 def process_patient_data(age, gender, ethnicity, geographic_location, diagnoses_icd10, medications, allergies, previous_treatments, blood_glucose_level, blood_pressure_systolic, blood_pressure_diastolic, bmi, smoking_status, alcohol_consumption, exercise_habits, diet, condition_severity, functional_status, previous_trial_participation):    
     
     # Encode the data
-    categorical_data = [gender, ethnicity, geographic_location, smoking_status, alcohol_consumption, exercise_habits, diet, functional_status, previous_trial_participation]
+    categorical_data = [gender, ethnicity, geographic_location, diagnoses_icd10, medications, allergies, previous_treatments, smoking_status, alcohol_consumption, exercise_habits, diet, functional_status, previous_trial_participation]
     print(f"Categorical data: {categorical_data}")
     encoded_categorical_data = encode_categorical_data(categorical_data)
     numerical_data = np.array([age, blood_glucose_level, blood_pressure_systolic, blood_pressure_diastolic, bmi, condition_severity])
     print(f"Numerical data: {numerical_data}")
     print(f"One-hot encoded data: {encoded_categorical_data}")
     combined_data = np.hstack((numerical_data, encoded_categorical_data))
+
+    ordered_categories = ["Gender", "Ethnicity", "Geographic_Location", "Diagnoses_ICD10", "Medications", "Allergies", "Previous_Treatments", "Smoking_Status", "Alcohol_Consumption", "Exercise_Habits", "Diet", "Functional_Status", "Previous_Trial_Participation"]
+    zipped_data = zip(ordered_categories, encoded_categorical_data)
+    # Convert the zipped data to a dictionary
+    encoded_categorical_dict = {category: value for category, value in zipped_data}
+
+    # Convert the data to JSON
+    json_data = clear_data_to_json({
+        "age": age,
+        "blood_glucose_level": blood_glucose_level,
+        "blood_pressure_systolic": blood_pressure_systolic,
+        "blood_pressure_diastolic": blood_pressure_diastolic,
+        "bmi": bmi,
+        "condition_severity": condition_severity,
+        **encoded_categorical_dict
+    })
+
+    print(f"JSON data: {json_data}")
     print(f"Combined data: {combined_data}")
-    encrypted_array = encrypt_array(combined_data, "user_id")
+    # encrypted_array = encrypt_array(combined_data, "user_id")
 
-    # Send the encrypted data to the server 
-    response = requests.post(SERVER_URL, data=encrypted_array)
+    # Send the data to the server 
+    url = SERVER_URL + "clear-match"
+    response = requests.post(url, data=json_data)
 
     # Check if the data was sent successfully
     if response.status_code == 200:
@@ -153,20 +201,19 @@ def process_patient_data(age, gender, ethnicity, geographic_location, diagnoses_
         print("Error sending data.")
     
     # Decrypt the result
-    decrypted_result = decrypt_result(response.content, USER_ID)
-    
+    # decrypted_result = decrypt_result(response.content, USER_ID)
+    print()
+    decrypted_result = response.json()
     # If the answer is True, return the link
     if decrypted_result:
         return (
-            f"Encrypted data: {encrypted_array}",
-            f"Decrypted result: {decrypted_result}",
-            f"You may now access the link to the [clinical trial]({EXAMPLE_CLINICAL_TRIAL_LINK})"
+            # f"Encrypted data: {encrypted_array}",
+            f"Decrypted result: {response.json()}"
         )
     else:
         return (
-            f"Encrypted data: {encrypted_array}",
-            f"Decrypted result: {decrypted_result}",
-            f"Unfortunately, there are no clinical trials available for the provided criteria."
+            # f"Encrypted data: {encrypted_array}",
+            f"Decrypted result: {response.json()}"
         )
 
 # Create the Gradio interface