app.py

from concrete.ml.deployment import FHEModelClient
from pathlib import Path
import numpy as np
import gradio as gr
import requests
import json

from typing import List

# Define possible categories for fields without predefined categories
additional_categories = {
    "Gender": ["Male", "Female", "Other"],
    "Ethnicity": ["White", "Black or African American", "Asian", "American Indian or Alaska Native", "Native Hawaiian or Other Pacific Islander", "Other"],
    "Geographic_Location": ["North America", "South America", "Europe", "Asia", "Africa", "Australia", "Antarctica"],
    "Smoking_Status": ["Never", "Former", "Current"],
    "Diagnoses_ICD10": ["E11.9", "I10", "J45.909", "M54.5", "F32.9", "K21.9"],
    "Medications": ["Metformin", "Lisinopril", "Atorvastatin", "Amlodipine", "Omeprazole", "Simvastatin", "Levothyroxine", "None"],
    "Allergies": ["Penicillin", "Peanuts", "Shellfish", "Latex", "Bee stings", "None"],
    "Previous_Treatments": ["Chemotherapy", "Radiation Therapy", "Surgery", "Physical Therapy", "Immunotherapy", "None"],
    "Alcohol_Consumption": ["None", "Occasionally", "Regularly", "Heavy"],
    "Exercise_Habits": ["Sedentary", "Light", "Moderate", "Active", "Very Active"],
    "Diet": ["Omnivore", "Vegetarian", "Vegan", "Pescatarian", "Keto", "Mediterranean"],
    "Functional_Status": ["Independent", "Assisted", "Dependent"],
    "Previous_Trial_Participation": ["Yes", "No"]
}

# Define the input components for the researcher form
min_age_input = gr.Number(label="Minimum Age", value=18)
max_age_input = gr.Number(label="Maximum Age", value=100)
gender_input = gr.CheckboxGroup(choices=additional_categories["Gender"], label="Gender")
ethnicity_input = gr.CheckboxGroup(choices=additional_categories["Ethnicity"], label="Ethnicity")
geographic_location_input = gr.CheckboxGroup(choices=additional_categories["Geographic_Location"], label="Geographic Location")
diagnoses_icd10_input = gr.CheckboxGroup(choices=additional_categories["Diagnoses_ICD10"], label="Diagnoses (ICD-10)")
medications_input = gr.CheckboxGroup(choices=additional_categories["Medications"], label="Medications")
allergies_input = gr.CheckboxGroup(choices=additional_categories["Allergies"], label="Allergies")
previous_treatments_input = gr.CheckboxGroup(choices=additional_categories["Previous_Treatments"], label="Previous Treatments")
min_blood_glucose_level_input = gr.Number(label="Minimum Blood Glucose Level", value=0)
max_blood_glucose_level_input = gr.Number(label="Maximum Blood Glucose Level", value=300)
min_blood_pressure_systolic_input = gr.Number(label="Minimum Blood Pressure (Systolic)", value=80)
max_blood_pressure_systolic_input = gr.Number(label="Maximum Blood Pressure (Systolic)", value=200)
min_blood_pressure_diastolic_input = gr.Number(label="Minimum Blood Pressure (Diastolic)", value=40)
max_blood_pressure_diastolic_input = gr.Number(label="Maximum Blood Pressure (Diastolic)", value=120)
min_bmi_input = gr.Number(label="Minimum BMI", value=10)
max_bmi_input = gr.Number(label="Maximum BMI", value=50)
smoking_status_input = gr.CheckboxGroup(choices=additional_categories["Smoking_Status"], label="Smoking Status")
alcohol_consumption_input = gr.CheckboxGroup(choices=additional_categories["Alcohol_Consumption"], label="Alcohol Consumption")
exercise_habits_input = gr.CheckboxGroup(choices=additional_categories["Exercise_Habits"], label="Exercise Habits")
diet_input = gr.CheckboxGroup(choices=additional_categories["Diet"], label="Diet")
min_condition_severity_input = gr.Number(label="Minimum Condition Severity", value=1)
max_condition_severity_input = gr.Number(label="Maximum Condition Severity", value=10)
functional_status_input = gr.CheckboxGroup(choices=additional_categories["Functional_Status"], label="Functional Status")
previous_trial_participation_input = gr.CheckboxGroup(choices=additional_categories["Previous_Trial_Participation"], label="Previous Trial Participation")


def encode_categorical_data(data: List[str], category_name: str) -> List[int]:
    """Encodes a list of categorical values into their corresponding indices based on additional_categories."""
    sub_cats = additional_categories.get(category_name, [])
    encoded_data = []
    for value in data:
        if value in sub_cats:
            encoded_data.append(sub_cats.index(value) + 1)  # Adding 1 to avoid index 0 for valid entries
        else:
            encoded_data.append(0)  # Encode unmatched as 0
    return encoded_data


def process_researcher_data(
    min_age, max_age, gender, ethnicity, geographic_location, diagnoses_icd10, medications, allergies, previous_treatments,
    min_blood_glucose_level, max_blood_glucose_level, min_blood_pressure_systolic, max_blood_pressure_systolic,
    min_blood_pressure_diastolic, max_blood_pressure_diastolic, min_bmi, max_bmi, smoking_status, alcohol_consumption,
    exercise_habits, diet, min_condition_severity, max_condition_severity, functional_status, previous_trial_participation
):
    # Encode categorical data
    encoded_gender = encode_categorical_data(gender, "Gender")
    encoded_ethnicity = encode_categorical_data(ethnicity, "Ethnicity")
    encoded_geographic_location = encode_categorical_data(geographic_location, "Geographic_Location")
    encoded_diagnoses_icd10 = encode_categorical_data(diagnoses_icd10, "Diagnoses_ICD10")
    encoded_smoking_status = encode_categorical_data(smoking_status, "Smoking_Status")
    encoded_alcohol_consumption = encode_categorical_data(alcohol_consumption, "Alcohol_Consumption")
    encoded_exercise_habits = encode_categorical_data(exercise_habits, "Exercise_Habits")
    encoded_diet = encode_categorical_data(diet, "Diet")
    encoded_functional_status = encode_categorical_data(functional_status, "Functional_Status")
    encoded_previous_trial_participation = encode_categorical_data(previous_trial_participation, "Previous_Trial_Participation")

    # Create a list of requirements
    requirements = []

    # Add numerical requirements
    if min_age is not None:
        requirements.append({
            "column_name": "Age",
            "value": int(min_age),
            "comparison_type": "greater_than"
        })
    if max_age is not None:
        requirements.append({
            "column_name": "Age",
            "value": int(max_age),
            "comparison_type": "less_than"
        })
    
    if min_blood_glucose_level is not None:
        requirements.append({
            "column_name": "Blood_Glucose_Level",
            "value": int(min_blood_glucose_level),
            "comparison_type": "greater_than"
        })
    if max_blood_glucose_level is not None:
        requirements.append({
            "column_name": "Blood_Glucose_Level",
            "value": int(max_blood_glucose_level),
            "comparison_type": "less_than"
        })
    
    if min_blood_pressure_systolic is not None:
        requirements.append({
            "column_name": "Blood_Pressure_Systolic",
            "value": int(min_blood_pressure_systolic),
            "comparison_type": "greater_than"
        })
    if max_blood_pressure_systolic is not None:
        requirements.append({
            "column_name": "Blood_Pressure_Systolic",
            "value": int(max_blood_pressure_systolic),
            "comparison_type": "less_than"
        })
    
    if min_blood_pressure_diastolic is not None:
        requirements.append({
            "column_name": "Blood_Pressure_Diastolic",
            "value": int(min_blood_pressure_diastolic),
            "comparison_type": "greater_than"
        })
    if max_blood_pressure_diastolic is not None:
        requirements.append({
            "column_name": "Blood_Pressure_Diastolic",
            "value": int(max_blood_pressure_diastolic),
            "comparison_type": "less_than"
        })
    
    if min_bmi is not None:
        requirements.append({
            "column_name": "BMI",
            "value": float(min_bmi),
            "comparison_type": "greater_than"
        })
    if max_bmi is not None:
        requirements.append({
            "column_name": "BMI",
            "value": float(max_bmi),
            "comparison_type": "less_than"
        })
    
    if min_condition_severity is not None:
        requirements.append({
            "column_name": "Condition_Severity",
            "value": int(min_condition_severity),
            "comparison_type": "greater_than"
        })
    if max_condition_severity is not None:
        requirements.append({
            "column_name": "Condition_Severity",
            "value": int(max_condition_severity),
            "comparison_type": "less_than"
        })

    # Add categorical requirements
    for gender_value in encoded_gender:
        if gender_value > 0:
            requirements.append({
                "column_name": "Gender",
                "value": gender_value,
                "comparison_type": "equal"
            })
    
    for ethnicity_value in encoded_ethnicity:
        if ethnicity_value > 0:
            requirements.append({
                "column_name": "Ethnicity",
                "value": ethnicity_value,
                "comparison_type": "equal"
            })

    for location_value in encoded_geographic_location:
        if location_value > 0:
            requirements.append({
                "column_name": "Geographic_Location",
                "value": location_value,
                "comparison_type": "equal"
            })

    for diagnosis_value in encoded_diagnoses_icd10:
        if diagnosis_value > 0:
            requirements.append({
                "column_name": "Diagnoses_ICD10",
                "value": diagnosis_value,
                "comparison_type": "equal"
            })

    for smoking_status_value in encoded_smoking_status:
        if smoking_status_value > 0:
            requirements.append({
                "column_name": "Smoking_Status",
                "value": smoking_status_value,
                "comparison_type": "equal"
            })

    for alcohol_value in encoded_alcohol_consumption:
        if alcohol_value > 0:
            requirements.append({
                "column_name": "Alcohol_Consumption",
                "value": alcohol_value,
                "comparison_type": "equal"
            })

    for exercise_value in encoded_exercise_habits:
        if exercise_value > 0:
            requirements.append({
                "column_name": "Exercise_Habits",
                "value": exercise_value,
                "comparison_type": "equal"
            })

    for diet_value in encoded_diet:
        if diet_value > 0:
            requirements.append({
                "column_name": "Diet",
                "value": diet_value,
                "comparison_type": "equal"
            })

    for status in encoded_functional_status:
        if status > 0:
            requirements.append({
                "column_name": "Functional_Status",
                "value": status,
                "comparison_type": "equal"
            })

    for participation in encoded_previous_trial_participation:
        if participation > 0:
            requirements.append({
                "column_name": "Previous_Trial_Participation",
                "value": participation,
                "comparison_type": "equal"
            })

    # Encode and add non-categorical fields like medications, allergies, previous treatments
    for medication in medications:
        encoded_medications = encode_categorical_data([medication], "Medications")
        for med_value in encoded_medications:
            if med_value > 0:
                requirements.append({
                    "column_name": "Medications",
                    "value": med_value,
                    "comparison_type": "equal"
                })

    for allergy in allergies:
        encoded_allergies = encode_categorical_data([allergy], "Allergies")
        for allergy_value in encoded_allergies:
            if allergy_value > 0:
                requirements.append({
                    "column_name": "Allergies",
                    "value": allergy_value,
                    "comparison_type": "equal"
                })

    for treatment in previous_treatments:
        encoded_treatments = encode_categorical_data([treatment], "Previous_Treatments")
        for treatment_value in encoded_treatments:
            if treatment_value > 0:
                requirements.append({
                    "column_name": "Previous_Treatments",
                    "value": treatment_value,
                    "comparison_type": "equal"
                })

    # Construct the payload as a regular dictionary
    payload = {
        "model_name": "fhe_model_v1",
        "requirements": requirements
    }

    # turn the payload into a JSON object
    payload = json.dumps(payload)

    print("Payload:", payload)

    # Store the server's URL
    SERVER_URL = "https://ppaihack-match.azurewebsites.net/requirements/create"

    # Make the request to the server
    try:
        res = requests.post(SERVER_URL, json=payload)
        res.raise_for_status()  # Raise an error for bad status codes
    except requests.exceptions.HTTPError as http_err:
        print(f"HTTP error occurred: {http_err}")  # For debugging
        return f"HTTP error occurred: {http_err}"
    except Exception as err:
        print(f"Other error occurred: {err}")  # For debugging
        return f"Other error occurred: {err}"

    # Get the response from the server
    try:
        response = res.json()
        print("Server response:", response)
    except ValueError:
        print("Response is not in JSON format.")
        return "Response is not in JSON format."

    return response.get("message", "No message received from server")


# Create the Gradio interface for researchers
researcher_demo = gr.Interface(
    fn=process_researcher_data, 
    inputs=[
        min_age_input, max_age_input, gender_input, ethnicity_input, geographic_location_input, diagnoses_icd10_input, 
        medications_input, allergies_input, previous_treatments_input, min_blood_glucose_level_input, 
        max_blood_glucose_level_input, min_blood_pressure_systolic_input, max_blood_pressure_systolic_input, 
        min_blood_pressure_diastolic_input, max_blood_pressure_diastolic_input, min_bmi_input, max_bmi_input, 
        smoking_status_input, alcohol_consumption_input, exercise_habits_input, diet_input, 
        min_condition_severity_input, max_condition_severity_input, functional_status_input, previous_trial_participation_input
    ], 
    outputs="text",
    title="Clinical Researcher Criteria Form",
    description="Please enter the criteria for the type of patients you are looking for."
)

# Launch the researcher interface with a public link
if __name__ == "__main__":
    researcher_demo.launch(share=True)