File size: 1,958 Bytes
ec24255 97407b6 ec24255 9dec907 ec24255 9dec907 ec24255 9dec907 ec24255 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
import gradio as gr
import pickle
import numpy as np
# Load your model from the .pkl file
with open('disease_prediction_model.pkl', 'rb') as model_file:
model = pickle.load(model_file)
# Define the prediction function
def predict_disease(age, gender, fever, cough, fatigue, difficulty_breathing, blood_pressure, cholesterol_level):
# Convert inputs to numerical or categorical as your model expects
gender = 0 if gender == "Male" else 1
fever = 1 if fever == "Yes" else 0
cough = 1 if cough == "Yes" else 0
fatigue = 1 if fatigue == "Yes" else 0
difficulty_breathing = 1 if difficulty_breathing == "Yes" else 0
blood_pressure_mapping = {"Low": 0, "Normal": 1, "High": 2}
cholesterol_level_mapping = {"Low": 0, "Normal": 1, "High": 2}
blood_pressure = blood_pressure_mapping[blood_pressure]
cholesterol_level = cholesterol_level_mapping[cholesterol_level]
# Assuming your model takes input as a NumPy array or similar format
input_features = np.array([[age, gender, fever, cough, fatigue, difficulty_breathing, blood_pressure, cholesterol_level]])
# Get the prediction
prediction = model.predict(input_features)
return prediction[0] # Return the predicted disease
# Gradio interface using new components
inputs = [
gr.Number(label="Age"),
gr.Radio(choices=["Male", "Female"], label="Gender"),
gr.Radio(choices=["Yes", "No"], label="Fever"),
gr.Radio(choices=["Yes", "No"], label="Cough"),
gr.Radio(choices=["Yes", "No"], label="Fatigue"),
gr.Radio(choices=["Yes", "No"], label="Difficulty Breathing"),
gr.Dropdown(choices=["Low", "Normal", "High"], label="Blood Pressure"),
gr.Dropdown(choices=["Low", "Normal", "High"], label="Cholesterol Level")
]
outputs = gr.Textbox(label="Predicted Disease")
# Create and launch the app
app = gr.Interface(fn=predict_disease, inputs=inputs, outputs=outputs, title="Symptom to Disease Prediction")
app.launch()
|