|
|
|
import joblib |
|
import numpy as np |
|
from flask import Flask, render_template, request, jsonify |
|
from sklearn.feature_extraction.text import CountVectorizer |
|
from sklearn.linear_model import LogisticRegression |
|
from pre_processing import preprocess_text |
|
|
|
app = Flask(__name__) |
|
|
|
|
|
model_path = "log_model_final.pkl" |
|
vectorizer_path = "vectorizer.pkl" |
|
|
|
|
|
model = joblib.load(model_path) |
|
vectorizer = joblib.load(vectorizer_path) |
|
|
|
@app.route('/') |
|
def index(): |
|
return render_template('index.html') |
|
|
|
@app.route('/predict', methods=['POST']) |
|
def predict(): |
|
|
|
try: |
|
|
|
input_text = request.get_json(force=True)['input'] |
|
print(input_text) |
|
test_data = vectorizer.transform([input_text]) |
|
y_pred = model.predict(test_data) |
|
print(y_pred) |
|
|
|
return jsonify({'prediction': y_pred[0]}) |
|
|
|
except Exception as e: |
|
return jsonify({'error': str(e)}) |
|
|
|
|
|
if __name__ == '__main__': |
|
app.run(host="0.0.0.0",port=5000) |
|
|