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Sleeping
File size: 2,142 Bytes
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from flask import Flask, request, render_template, send_file, jsonify
import joblib
import pandas as pd
import numpy as np
import io
app = Flask(__name__)
# Load model and scaler
model = joblib.load("heart_model.pkl")
scaler = joblib.load("scaler.pkl")
FEATURES = ['age','sex','cp','trestbps','chol','fbs','restecg',
'thalach','exang','oldpeak','slope','ca','thal']
@app.route("/")
def home():
return render_template("index.html")
# JSON endpoint for dynamic prediction
@app.route("/predict_json", methods=["POST"])
def predict_json():
try:
data_dict = request.get_json()
data_df = pd.DataFrame([data_dict], columns=FEATURES)
data_scaled = scaler.transform(data_df)
pred = model.predict(data_scaled)[0]
result = "No Heart Disease ✅" if pred == 0 else "Heart Disease ❌"
return jsonify({"prediction": result})
except Exception as e:
return jsonify({"error": str(e)})
# Batch CSV prediction
@app.route("/batch_predict", methods=["POST"])
def batch_predict():
try:
file = request.files['file']
if not file:
return render_template("index.html", result="No file uploaded")
df = pd.read_csv(file)
if not all(col in df.columns for col in FEATURES):
return render_template("index.html", result="CSV must have all required columns!")
X_scaled = scaler.transform(df[FEATURES])
preds = model.predict(X_scaled)
df["prediction"] = ["No Heart Disease ✅" if p==0 else "Heart Disease ❌" for p in preds]
# Return CSV as download
output = io.StringIO()
df.to_csv(output, index=False)
output.seek(0)
return send_file(io.BytesIO(output.getvalue().encode()),
mimetype="text/csv",
as_attachment=True,
download_name="batch_predictions.csv")
except Exception as e:
return render_template("index.html", result=f"Error: {str(e)}")
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
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