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d07d826 f1509c0 d07d826 | 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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | import pandas as pd
import numpy as np
import os
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from flask import Flask, render_template, request, jsonify
app = Flask(__name__)
# Global variables
model = None
scaler = None
# Initilize Model
def initialize_model():
global model, scaler
# Locate CSV File
possible_paths = ["diabetes.csv", "src/diabetes.csv", "../diabetes.csv"]
data_path = None
for path in possible_paths:
if os.path.exists(path):
data_path = path
break
if not data_path:
print("❌ CRITICAL: 'diabetes.csv' not found.")
return
# Load and Prepare Data
try:
df = pd.read_csv(data_path)
X = df.drop('Outcome', axis=1)
y = df['Outcome']
# Scaling (Crucial for KNN)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# rain KNN Model
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
model = KNeighborsClassifier(n_neighbors=9)
model.fit(X_train, y_train)
print("✅ Model trained successfully.")
except Exception as e:
print(f"❌ Error initializing model: {e}")
# Run initialization
initialize_model()
# File Routes
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if not model:
return jsonify({'error': 'Model not loaded'}), 500
try:
data = request.json
# Do not change the Order
input_features = [
float(data['Pregnancies']),
float(data['Glucose']),
float(data['BloodPressure']),
float(data['SkinThickness']),
float(data['Insulin']),
float(data['BMI']),
float(data['DiabetesPedigreeFunction']),
float(data['Age'])
]
# Scale input
features_scaled = scaler.transform([input_features])
# Predict
prediction = model.predict(features_scaled)
prob = model.predict_proba(features_scaled)
result = int(prediction[0])
confidence = prob[0][result] * 100
return jsonify({'prediction': result, 'confidence': confidence})
except Exception as e:
return jsonify({'error': str(e)}), 400
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860) |