Upload 5 files

AI-only_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f96b099ef6175584823e331e985de53af52d50a7e22617d9308a25196db69c58
+size 267873906

Dockerfile

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+# Use the official Python image as a base image
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+# Set environment variables
+ENV FLASK_APP=app.py
+ENV FLASK_RUN_HOST=0.0.0.0
+ENV FLASK_RUN_PORT=5001
+
+# Expose port 5000 to allow external connections
+EXPOSE 5001
+
+# Run the Flask app
+CMD ["flask", "run"]

Human-in-the-loop_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f22501f82dbd0fc88295abe5b828b302f12a67350521d36eb1f456f097188e30
+size 267873906

app.py

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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)

requirements.txt

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+python-dotenv
+flask == 2.2.2
+werkzeug == 2.2.2
+flask-cors
+pandas
+torch
+transformers == 4.40.0
+