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| from flask import Flask, jsonify, request, send_from_directory | |
| from flask_cors import CORS | |
| import pandas as pd | |
| import os | |
| import pickle | |
| from transformers import DistilBertTokenizer | |
| import torch | |
| import numpy as np | |
| # from your_model import load_model, predict_response # assuming these functions are implemented | |
| app = Flask(__name__) | |
| # Load your machine learning model | |
| # ai_only = load_model('ai_model_path.pkl') | |
| # hitl = load_model('hitl_model_path.pkl') | |
| # Load your questions and responses | |
| questions_df = pd.read_csv('data/questions.csv') | |
| # responses_df = pd.read_csv('data/responses.csv') | |
| CORS(app, resources={r"/api/*": {"origins": "*"}}) | |
| react_folder= 'frontend' | |
| directory= os.getcwd()+ f'/{react_folder}/build/static/' | |
| def load_model(name): | |
| print("Loading model") | |
| with open(f'{name}_model.pkl', 'rb') as f: | |
| loaded_model = pickle.load(f) | |
| print("model loaded successfully") | |
| return loaded_model | |
| def make_prediction(model, data): | |
| model= load_model(model) | |
| tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') | |
| inputs = tokenizer(data, return_tensors="pt", truncation=True, padding=True, max_length=136) | |
| # Forward pass through the model to obtain logits | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| # Get predicted probabilities using softmax function | |
| probs = torch.softmax(outputs.logits, dim=1) | |
| # Get predicted class (0 or 1) | |
| predicted_class = torch.argmax(probs, dim=1).item() | |
| # Map predicted class to corresponding label | |
| predicted_label = "AI" if predicted_class == 0 else "Human" | |
| print("Predicted Label:", predicted_label) | |
| print("Probability:", probs) | |
| return [predicted_label, max(probs[0][0], probs[0][1])] | |
| def index(): | |
| path = os.getcwd()+f'/{react_folder}/build' | |
| print(path) | |
| return send_from_directory(directory=path, path='index.html') | |
| def css(folder,file): | |
| path = folder+'/'+file | |
| return send_from_directory(directory= directory, path= path) | |
| def css(folder,file): | |
| directory = os.getcwd()+f'/{react_folder}/build/assets' | |
| path = file | |
| return send_from_directory(directory=directory, path= path) | |
| # @app.route('/api/questions', methods=['GET']) | |
| # def get_questions(): | |
| # # Sending a list of questions along with their IDs | |
| # questions = questions_df[['question_id', 'question']].to_dict(orient='records') | |
| # return jsonify(questions) | |
| def get_random_question(): | |
| # Fetching a random question from the dataframe | |
| random_question = questions_df.sample().iloc[0][1] | |
| print(random_question) | |
| return jsonify(random_question) | |
| def predict(): | |
| # Get response from the request | |
| data = request.json | |
| response_text = data['response'] | |
| model_type = data['mode'] | |
| print('input = ', response_text) | |
| print('mode=',model_type) | |
| #make_prediction(model_type,response_text) | |
| # Perform prediction | |
| # prediction_label, confidence = predict_response(model, response_text) | |
| prediction_label, confidence = make_prediction(model_type, response_text) | |
| if not prediction_label: | |
| prediction_label = 'AI' | |
| if not confidence: | |
| confidence = 0 | |
| return jsonify({ | |
| 'prediction': prediction_label, | |
| 'confidence': f'{confidence*100:.2f}%' | |
| }) | |
| if __name__ == '__main__': | |
| app.run(host='0.0.0.0', port=5001) |