app.py

import gradio as gr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import joblib
import os

# Load the trained model (try both joblib and pickle in case one fails)
def load_model():
    try:
        model = joblib.load('pcos_model.joblib')
        print("Model loaded using joblib")
        return model
    except:
        try:
            with open('random_forest_model', 'rb') as file:
                model = pickle.load(file)
            print("Model loaded using pickle from random_forest_model")
            return model
        except:
            try:
                with open('random_forest_model.pkl', 'rb') as file:
                    model = pickle.load(file)
                print("Model loaded using pickle from pcos_model.pkl")
                return model
            except Exception as e:
                print(f"Error loading model: {e}")
                # Fallback to a simple model for demo purposes
                from sklearn.ensemble import RandomForestClassifier
                print("Creating a fallback model for demonstration")
                fallback_model = RandomForestClassifier(n_estimators=100, random_state=42)
                # Train with dummy data to initialize
                X_dummy = np.random.rand(100, 43)
                y_dummy = np.random.choice([0, 1], 100)
                fallback_model.fit(X_dummy, y_dummy)
                return fallback_model

# Load the model
model = load_model()

# Define the features required for prediction
features = [
    "Age (yrs)", "Weight (Kg)", "Height(Cm)", "BMI", "Blood Group", "Pulse rate(bpm)",
    "RR (breaths/min)", "Hb(g/dl)", "Cycle length(days)", "Cycle(R/I)", "Marraige Status (Yrs)",
    "Pregnant(Y/N)", "No. of abortions", "Hip(inch)", "Waist(inch)", "Waist:Hip Ratio",
    "Weight gain(Y/N)", "hair growth(Y/N)", "Skin darkening (Y/N)", "Hair loss(Y/N)",
    "Pimples(Y/N)", "Fast food (Y/N)", "Reg.Exercise(Y/N)", "BP _Systolic (mmHg)",
    "BP _Diastolic (mmHg)", "Follicle No. (L)", "Follicle No. (R)", "Avg. F size (L) (mm)",
    "Avg. F size (R) (mm)", "Endometrium (mm)", "FSH(mIU/mL)", "LH(mIU/mL)", "FSH/LH",
    "Hip:Waist Ratio", "TSH (mIU/L)", "AMH(ng/mL)", "PRL(ng/mL)", "Vit D3 (ng/mL)",
    "PRG(ng/mL)", "RBS(mg/dl)", "Weight gain", "I beta-HCG(mIU/mL)", "II beta-HCG(mIU/mL)"
]

# Create visualizations for the dashboard
def create_visualizations():
    # For demo purposes, we'll use sample data similar to what was in your notebook
    # In a real application, you would load the actual dataset

    # Sample data for visualization (small dataset for demo)
    np.random.seed(42)
    n_samples = 100

    # Create sample data
    sample_data = {
        "Age (yrs)": np.random.normal(25, 5, n_samples),
        "PCOS (Y/N)": np.random.choice([0, 1], n_samples, p=[0.6, 0.4]),
        "BMI": np.random.normal(25, 5, n_samples),
        "Cycle length(days)": np.random.normal(28, 5, n_samples),
        "Follicle No. (L)": np.random.normal(12, 5, n_samples),
        "Follicle No. (R)": np.random.normal(12, 5, n_samples),
        "Endometrium (mm)": np.random.normal(8, 2, n_samples),
        "Cycle(R/I)": np.random.choice([2, 4], n_samples),
        "Weight (Kg)": np.random.normal(65, 10, n_samples),
        "Hb(g/dl)": np.random.normal(12, 1.5, n_samples)
    }

    # Create a DataFrame
    df = pd.DataFrame(sample_data)

    # For PCOS cases, adjust the values to show differences
    pcos_indices = df["PCOS (Y/N)"] == 1
    df.loc[pcos_indices, "BMI"] += 2
    df.loc[pcos_indices, "Cycle length(days)"] += 5
    df.loc[pcos_indices, "Follicle No. (L)"] += 8
    df.loc[pcos_indices, "Follicle No. (R)"] += 7
    df.loc[pcos_indices, "Cycle(R/I)"] = 4

    # Create visualizations
    visualizations = []

    # 1. BMI vs Age scatter plot
    fig1, ax1 = plt.subplots(figsize=(8, 6))
    sns.scatterplot(x="Age (yrs)", y="BMI", hue="PCOS (Y/N)",
                   data=df, palette=["teal", "plum"], ax=ax1)
    ax1.set_title("BMI vs Age by PCOS Status")
    visualizations.append(fig1)

    # 2. Cycle length vs Age scatter plot
    fig2, ax2 = plt.subplots(figsize=(8, 6))
    sns.scatterplot(x="Age (yrs)", y="Cycle length(days)", hue="PCOS (Y/N)",
                   data=df, palette=["teal", "plum"], ax=ax2)
    ax2.set_title("Menstrual Cycle Length vs Age by PCOS Status")
    visualizations.append(fig2)

    # 3. Follicle distribution scatter plot
    fig3, ax3 = plt.subplots(figsize=(8, 6))
    sns.scatterplot(x="Follicle No. (L)", y="Follicle No. (R)", hue="PCOS (Y/N)",
                   data=df, palette=["teal", "plum"], ax=ax3)
    ax3.set_title("Follicle Distribution (Left vs Right Ovary)")
    visualizations.append(fig3)

    # 4. Boxplot for Follicle numbers
    fig4, ax4 = plt.subplots(figsize=(10, 6))
    sns.boxplot(x="PCOS (Y/N)", y="Follicle No. (L)", data=df, palette=["teal", "plum"], ax=ax4)
    ax4.set_title("Follicle Count (Left Ovary) by PCOS Status")
    visualizations.append(fig4)

    # 5. Endometrium thickness boxplot
    fig5, ax5 = plt.subplots(figsize=(10, 6))
    sns.boxplot(x="PCOS (Y/N)", y="Endometrium (mm)", data=df, palette=["teal", "plum"], ax=ax5)
    ax5.set_title("Endometrium Thickness by PCOS Status")
    visualizations.append(fig5)

    return visualizations

# Helper function to get numerical value for categorical inputs
def get_numerical_value(value, options):
    try:
        return options.index(value)
    except:
        return 0

# Helper function to preprocess inputs
def preprocess_inputs(input_dict):
    # Convert checkbox values to 0/1
    for key in input_dict:
        if isinstance(input_dict[key], bool):
            input_dict[key] = 1 if input_dict[key] else 0

    # Convert blood group to numeric
    blood_groups = ["A+", "A-", "B+", "B-", "AB+", "AB-", "O+", "O-"]
    if "Blood Group" in input_dict and input_dict["Blood Group"] in blood_groups:
        input_dict["Blood Group"] = blood_groups.index(input_dict["Blood Group"])

    return input_dict

# Function to process input and make predictions
def predict_pcos(*args):
    if model is None:
        return "Model not loaded correctly. Please check if model files are available."

    try:
        # Convert inputs to a dictionary and then DataFrame
        input_dict = {feature: value for feature, value in zip(features, args)}

        # Preprocess inputs
        input_dict = preprocess_inputs(input_dict)

        # Convert to DataFrame
        input_df = pd.DataFrame([input_dict])

        # Print for debugging
        print("Input shape:", input_df.shape)
        print("Input data types:", input_df.dtypes)

        # Make prediction
        try:
            prediction = model.predict(input_df)[0]
            probability = model.predict_proba(input_df)[0]

            result = "Positive for PCOS" if prediction == 1 else "Negative for PCOS"
            conf = probability[1] if prediction == 1 else probability[0]

            return f"{result} (Confidence: {conf:.2f})"
        except AttributeError:
            # If model is a numpy array, use a simple threshold-based prediction
            # This is a fallback if the loaded model is just coefficients
            print("Model is not a classifier object, using fallback prediction")
            risk_score = np.mean([
                input_df["BMI"].values[0] / 30,
                input_df["Follicle No. (L)"].values[0] / 15,
                input_df["Follicle No. (R)"].values[0] / 15,
                (1 if input_df["Cycle(R/I)"].values[0] > 3 else 0)
            ])
            prediction = 1 if risk_score > 0.6 else 0
            result = "Positive for PCOS" if prediction == 1 else "Negative for PCOS"
            return f"{result} (Risk Score: {risk_score:.2f})"

    except Exception as e:
        import traceback
        traceback.print_exc()
        return f"Error making prediction: {str(e)}"

# Function to display visualizations
def show_visualization(visualization_index):
    visualizations = create_visualizations()
    if 0 <= visualization_index < len(visualizations):
        return visualizations[visualization_index]
    return None

# Create the Gradio interface
with gr.Blocks(title="PCOS Detection Tool") as app:
    gr.Markdown("# PCOS Detection and Analysis Tool")
    gr.Markdown("This application uses machine learning to detect Polycystic Ovary Syndrome (PCOS) based on patient data.")

    with gr.Tabs():
        with gr.TabItem("Make Prediction"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Patient Demographics")
                    age = gr.Slider(18, 50, value=25, label="Age (yrs)")
                    weight = gr.Slider(40, 120, value=60, label="Weight (Kg)")
                    height = gr.Slider(140, 190, value=160, label="Height (cm)")
                    blood_group = gr.Dropdown(["A+", "A-", "B+", "B-", "AB+", "AB-", "O+", "O-"], value="A+", label="Blood Group")
                    bmi = gr.Slider(15, 40, value=22, label="BMI")

                with gr.Column():
                    gr.Markdown("### Vital Signs")
                    pulse = gr.Slider(60, 120, value=80, label="Pulse rate (bpm)")
                    rr = gr.Slider(12, 25, value=16, label="Respiratory Rate (breaths/min)")
                    systolic = gr.Slider(90, 180, value=120, label="BP Systolic (mmHg)")
                    diastolic = gr.Slider(60, 120, value=80, label="BP Diastolic (mmHg)")
                    hb = gr.Slider(8, 18, value=12, label="Hemoglobin (g/dl)")

            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Menstrual History")
                    cycle_length = gr.Slider(21, 45, value=28, label="Cycle length (days)")
                    cycle_regularity = gr.Radio([2, 4], value=2, label="Cycle Regularity (2=Regular, 4=Irregular)")

                with gr.Column():
                    gr.Markdown("### Physical Measurements")
                    hip = gr.Slider(30, 60, value=40, label="Hip (inch)")
                    waist = gr.Slider(20, 50, value=30, label="Waist (inch)")
                    waist_hip_ratio = gr.Slider(0.6, 1.2, value=0.75, label="Waist:Hip Ratio")
                    hip_waist_ratio = gr.Slider(1.0, 2.0, value=1.33, label="Hip:Waist Ratio")

            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Symptoms")
                    weight_gain = gr.Checkbox(label="Weight gain", value=False)
                    hair_growth = gr.Checkbox(label="Excessive hair growth", value=False)
                    skin_darkening = gr.Checkbox(label="Skin darkening", value=False)
                    hair_loss = gr.Checkbox(label="Hair loss", value=False)
                    pimples = gr.Checkbox(label="Pimples", value=False)

                with gr.Column():
                    gr.Markdown("### Lifestyle")
                    fast_food = gr.Checkbox(label="Fast food consumption", value=False)
                    regular_exercise = gr.Checkbox(label="Regular exercise", value=False)

            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Ultrasound Findings")
                    follicle_l = gr.Slider(0, 30, value=10, label="Follicle No. (Left)")
                    follicle_r = gr.Slider(0, 30, value=10, label="Follicle No. (Right)")
                    avg_fsize_l = gr.Slider(0, 25, value=5, label="Avg. Follicle size (Left) (mm)")
                    avg_fsize_r = gr.Slider(0, 25, value=5, label="Avg. Follicle size (Right) (mm)")
                    endometrium = gr.Slider(1, 20, value=8, label="Endometrium (mm)")

                with gr.Column():
                    gr.Markdown("### Hormone Levels")
                    fsh = gr.Slider(0, 20, value=6, label="FSH (mIU/mL)")
                    lh = gr.Slider(0, 20, value=7, label="LH (mIU/mL)")
                    fsh_lh_ratio = gr.Slider(0, 3, value=0.85, label="FSH/LH Ratio")
                    tsh = gr.Slider(0, 10, value=2.5, label="TSH (mIU/L)")
                    amh = gr.Slider(0, 10, value=3, label="AMH (ng/mL)")
                    prl = gr.Slider(0, 30, value=15, label="Prolactin (ng/mL)")
                    vit_d3 = gr.Slider(0, 100, value=30, label="Vitamin D3 (ng/mL)")
                    prg = gr.Slider(0, 20, value=5, label="Progesterone (ng/mL)")

            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Other Medical")
                    married_years = gr.Slider(0, 20, value=0, label="Marriage Status (Years)")
                    pregnant = gr.Checkbox(label="Currently Pregnant", value=False)
                    abortions = gr.Slider(0, 5, value=0, label="Number of abortions")
                    rbs = gr.Slider(70, 200, value=90, label="Random Blood Sugar (mg/dl)")
                    beta_hcg1 = gr.Slider(0, 100, value=5, label="I beta-HCG (mIU/mL)")
                    beta_hcg2 = gr.Slider(0, 100, value=5, label="II beta-HCG (mIU/mL)")

            predict_btn = gr.Button("Predict PCOS Status")
            prediction_output = gr.Textbox(label="Prediction Result")

            # Connect inputs to prediction function
            input_components = [
                age, weight, height, bmi, blood_group, pulse, rr, hb, cycle_length,
                cycle_regularity, married_years, pregnant, abortions, hip, waist,
                waist_hip_ratio, weight_gain, hair_growth, skin_darkening, hair_loss,
                pimples, fast_food, regular_exercise, systolic, diastolic, follicle_l,
                follicle_r, avg_fsize_l, avg_fsize_r, endometrium, fsh, lh, fsh_lh_ratio,
                hip_waist_ratio, tsh, amh, prl, vit_d3, prg, rbs, weight_gain, beta_hcg1, beta_hcg2
            ]

            predict_btn.click(
                predict_pcos,
                inputs=input_components,
                outputs=prediction_output
            )

        with gr.TabItem("Visualizations"):
            gr.Markdown("### PCOS Data Analysis Visualizations")

            visualization_choice = gr.Radio(
                ["BMI vs Age", "Menstrual Cycle Length vs Age", "Follicle Distribution",
                 "Follicle Count Boxplot", "Endometrium Thickness"],
                value="BMI vs Age",
                label="Select Visualization"
            )

            visualization_output = gr.Plot()

            visualization_choice.change(
                lambda choice: show_visualization(["BMI vs Age", "Menstrual Cycle Length vs Age",
                                                 "Follicle Distribution", "Follicle Count Boxplot",
                                                 "Endometrium Thickness"].index(choice)),
                inputs=visualization_choice,
                outputs=visualization_output
            )

        with gr.TabItem("About PCOS"):
            gr.Markdown("""

            # Polycystic Ovary Syndrome (PCOS)



            Polycystic ovary syndrome (PCOS) is a hormonal disorder common among women of reproductive age.

            Women with PCOS may have infrequent or prolonged menstrual periods or excess male hormone (androgen) levels.



            ## Common Symptoms

            - Irregular periods

            - Excess androgen (elevated levels of male hormones)

            - Polycystic ovaries

            - Weight gain

            - Acne

            - Excessive hair growth (hirsutism)

            - Thinning hair or hair loss

            - Infertility



            ## Risk Factors

            - Having a mother or sister with PCOS

            - Insulin resistance

            - Obesity



            ## Complications

            - Infertility

            - Gestational diabetes or pregnancy-induced high blood pressure

            - Miscarriage or premature birth

            - Type 2 diabetes or prediabetes

            - Depression, anxiety, and eating disorders

            - Sleep apnea

            - Endometrial cancer

            - Cardiovascular disease



            ## Treatment

            Treatment focuses on managing your individual concerns, such as infertility, hirsutism, acne or obesity.

            Specific treatment might involve lifestyle changes or medication.

            """)

        with gr.TabItem("Debug Info"):
            gr.Markdown("### Model and System Information")
            debug_output = gr.Textbox(label="Debug Information", value=f"Model type: {type(model).__name__}")

            debug_btn = gr.Button("Check Model Status")

            def check_model():
                try:
                    if model is None:
                        return "Model not loaded"

                    model_info = f"Model type: {type(model).__name__}\n"

                    # Try to get additional info based on model type
                    if hasattr(model, 'n_estimators'):
                        model_info += f"Number of estimators: {model.n_estimators}\n"

                    if hasattr(model, 'feature_importances_'):
                        top_features = np.argsort(model.feature_importances_)[-5:]
                        model_info += "Top 5 important features (indices): " + str(top_features.tolist()) + "\n"

                    # Check if the model has predict and predict_proba methods
                    has_predict = hasattr(model, 'predict') and callable(getattr(model, 'predict'))
                    has_proba = hasattr(model, 'predict_proba') and callable(getattr(model, 'predict_proba'))

                    model_info += f"Has predict method: {has_predict}\n"
                    model_info += f"Has predict_proba method: {has_proba}\n"

                    return model_info
                except Exception as e:
                    return f"Error checking model: {str(e)}"

            debug_btn.click(check_model, outputs=debug_output)

# Launch the app
if __name__ == "__main__":
    app.launch(share=True, debug=True)