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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])]
@app.route('/DilemmaAI', endpoint='func1')
def index():
path = os.getcwd()+f'/{react_folder}/build'
print(path)
return send_from_directory(directory=path, path='index.html')
@app.route('/static/<folder>/<file>')
def css(folder,file):
path = folder+'/'+file
return send_from_directory(directory= directory, path= path)
@app.route('/assets/<file>', endpoint='func2')
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)
@app.route('/api/random-question', methods=['GET'])
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)
@app.route('/api/predict', methods=['POST'])
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) |