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import gradio as gr
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
# Load datasets
def load_data():
df = pd.read_csv("Training.csv")
tr = pd.read_csv("Testing.csv")
# Encode diseases
disease_dict = {
'Fungal infection': 0, 'Allergy': 1, 'GERD': 2, 'Chronic cholestasis': 3, 'Drug Reaction': 4,
'Peptic ulcer diseae': 5, 'AIDS': 6, 'Diabetes ': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
'Hypertension ': 10, 'Migraine': 11, 'Cervical spondylosis': 12, 'Paralysis (brain hemorrhage)': 13,
'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18, 'hepatitis A': 19,
'Hepatitis B': 20, 'Hepatitis C': 21, 'Hepatitis D': 22, 'Hepatitis E': 23, 'Alcoholic hepatitis': 24,
'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27, 'Dimorphic hemmorhoids(piles)': 28,
'Heart attack': 29, 'Varicose veins': 30, 'Hypothyroidism': 31, 'Hyperthyroidism': 32,
'Hypoglycemia': 33, 'Osteoarthristis': 34, 'Arthritis': 35,
'(vertigo) Paroymsal Positional Vertigo': 36, 'Acne': 37, 'Urinary tract infection': 38,
'Psoriasis': 39, 'Impetigo': 40
}
df.replace({'prognosis': disease_dict}, inplace=True)
df = df.infer_objects(copy=False)
tr.replace({'prognosis': disease_dict}, inplace=True)
tr = tr.infer_objects(copy=False)
return df, tr, disease_dict
try:
df, tr, disease_dict = load_data()
except FileNotFoundError as e:
raise RuntimeError("Data files not found. Please ensure `Training.csv` and `Testing.csv` are available.")
except Exception as e:
raise RuntimeError(f"An error occurred while loading data: {e}")
l1 = list(df.columns[:-1])
X = df[l1]
y = df['prognosis']
X_test = tr[l1]
y_test = tr['prognosis']
# Trained models
def train_models():
models = {
"Decision Tree": DecisionTreeClassifier(),
"Random Forest": RandomForestClassifier(),
"Naive Bayes": GaussianNB()
}
trained_models = {}
for model_name, model_obj in models.items():
model_obj.fit(X, y)
acc = accuracy_score(y_test, model_obj.predict(X_test))
trained_models[model_name] = (model_obj, acc)
return trained_models
trained_models = train_models()
def predict_disease(model, symptoms):
input_test = np.zeros(len(l1))
for symptom in symptoms:
if symptom in l1:
input_test[l1.index(symptom)] = 1
prediction = model.predict([input_test])[0]
return list(disease_dict.keys())[list(disease_dict.values()).index(prediction)]
# Gradio Interface
def app_function(name, symptom1, symptom2, symptom3, symptom4, symptom5):
if not name.strip():
return "Please enter the patient's name."
symptoms_selected = [s for s in [symptom1, symptom2, symptom3, symptom4, symptom5] if s != "None"]
if len(symptoms_selected) < 3:
return "Please select at least 3 symptoms for accurate prediction."
results = []
for model_name, (model, acc) in trained_models.items():
prediction = predict_disease(model, symptoms_selected)
result = f"{model_name} Prediction: Predicted Disease: **{prediction}**"
result += f" (Accuracy: {acc * 100:.2f}%)"
results.append(result)
return "\n\n".join(results)
# Gradio Interface Setup
iface = gr.Interface(
fn=app_function,
inputs=[
gr.Textbox(label="Name of Patient"),
gr.Dropdown(["None"] + l1, label="Symptom 1"),
gr.Dropdown(["None"] + l1, label="Symptom 2"),
gr.Dropdown(["None"] + l1, label="Symptom 3"),
gr.Dropdown(["None"] + l1, label="Symptom 4"),
gr.Dropdown(["None"] + l1, label="Symptom 5"),
],
outputs=gr.Textbox(label="Prediction"),
title="Disease Predictor Using Machine Learning",
description="For accurate results, please select at least 3 symptoms.",
article="**Caution:** This system is designed for informational purposes only. Please visit a healthcare provider for any medical concerns."
)
# Launch the Gradio application
iface.launch() |