Delete api.py

api.py

@@ -1,92 +0,0 @@
-from flask import Flask, request, jsonify
-import numpy as np
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn import svm
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.ensemble import GradientBoostingClassifier
-from xgboost import XGBClassifier
-from sklearn.metrics import accuracy_score
-import joblib
-import pickle
-
-app = Flask(__name__)
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    data = request.get_json()
-
-    # Load trained models
-    with open('rf_hacathon_fullstk.pkl', 'rb') as f1:
-        rf_fullstk = pickle.load(f1)
-    with open('rf_hacathon_prodengg.pkl', 'rb') as f2:
-        rf_prodengg = pickle.load(f2)
-    with open('rf_hacathon_mkt.pkl', 'rb') as f3:
-        rf_mkt = pickle.load(f3)
-
-    # Extract input features
-    new_data_fullstk = pd.DataFrame({
-        'degree_p': data['degree_p'],
-        'internship': data['internship'],
-        'DSA': data['DSA'],
-        'java': data['java'],
-    }, index=[0])
-
-    new_data_prodengg = pd.DataFrame({
-        'degree_p': data['degree_p'],
-        'internship': data['internship'],
-        'management': data['management'],
-        'leadership': data['leadership'],
-    }, index=[0])
-
-    new_data_mkt = pd.DataFrame({
-        'degree_p': data['degree_p'],
-        'internship': data['internship'],
-        'communication': data['communication'],
-        'sales': data['sales'],
-    }, index=[0])
-
-    # Make predictions
-    p_prodeng = rf_prodengg.predict(new_data_prodengg)
-    prob_prdeng = rf_prodengg.predict_proba(new_data_prodengg)
-    if p_prodeng == 1:
-        pred_prodeng = 'Placed'
-        prob_prodeng = prob_prdeng[0][1]
-    else:
-        pred_prodeng = 'Not-placed'
-        prob_prodeng = prob_prdeng[0][0]
-
-    p_fstk = rf_fullstk.predict(new_data_fullstk)
-    prob_fstk = rf_fullstk.predict_proba(new_data_fullstk)
-    if p_fstk == 1:
-        pred_fstk = 'Placed'
-        prob_fstk = prob_fstk[0][1]
-    else:
-        pred_fstk = 'Not-placed'
-        prob_fstk = prob_fstk[0][0]
-
-    p_mkt = rf_mkt.predict(new_data_mkt)
-    prob_mkt = rf_mkt.predict_proba(new_data_mkt)
-    if p_mkt == 1:
-        pred_mkt = 'Placed'
-        prob_mkt = prob_mkt[0][1]
-    else:
-        pred_mkt = 'Not-placed'
-        prob_mkt = prob_mkt[0][0]
-
-    result = {
-        'prediction_fullstk': pred_fstk,
-        'probability_fullstk': prob_fstk,
-        'prediction_prodengg': pred_prodeng,
-        'probability_prodengg': prob_prodeng,
-        'prediction_mkt': pred_mkt,
-        'probability_mkt': prob_mkt
-    }
-
-    return jsonify(result)
-
-if __name__ == '__main__':
-    app.run(debug=True)