added worker validation bot
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OwusuBlessing
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startxref
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@@ -2,6 +2,7 @@ import os
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from flask import Flask
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from src.api.routes.sops import sops_bp
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from src.api.routes.questions import qs_b
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from src.api.routes.chatbot import bot
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def create_app():
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app = Flask(__name__)
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@@ -9,6 +10,7 @@ def create_app():
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# Register the blueprint with the desired prefix
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app.register_blueprint(sops_bp, url_prefix='/api/v1/sop')
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app.register_blueprint(qs_b,url_prefix='/api/v1/qs')
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app.register_blueprint(bot,url_prefix='/api/v1/bot')
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# Set up the upload folder configuration inside the src directory
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UPLOAD_FOLDER = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../uploads')
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@@ -0,0 +1,61 @@
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import os
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from flask import Blueprint, request, jsonify, current_app
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from werkzeug.utils import secure_filename
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from src.services.chatbot import Chatbot
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from src.utils.utils import delete_all_files_in_directory
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from src.utils.document_loader import load_document
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# Initialize the Blueprint
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bot = Blueprint('chatbot', __name__)
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# Allowed file extensions
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ALLOWED_EXTENSIONS = {'pdf', 'doc', 'docx'}
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def allowed_file(filename):
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"""Check if the file has an allowed extension."""
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return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
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@bot.route('/validate_worker_document', methods=['POST'])
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def validate_worker_document():
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try:
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# Retrieve form data
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question = request.form.get('question')
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file = request.files.get('document')
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if not question or not file:
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return jsonify({"error": "Missing data", "message": "Question or document not provided."}), 400
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if file.filename == '':
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return jsonify({"error": "No selected file", "message": "No file was selected for upload."}), 400
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if file and allowed_file(file.filename):
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# Secure the file name and save it
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filename = secure_filename(file.filename)
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upload_folder = current_app.config['UPLOAD_FOLDER']
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file_path = os.path.join(upload_folder, filename)
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file.save(file_path)
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# Load the document for processing
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docs = load_document(file_path)
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# Instantiate the chatbot service
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chatbot = Chatbot()
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# Validate the worker's response using the provided document
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validation_result = chatbot.validate_worker(question, docs)
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if not validation_result:
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return jsonify({"error": "Validation failed", "message": "Validation process failed."}), 400
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# Clean up uploaded file (optional)
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os.remove(file_path)
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# Return the validation result
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return jsonify({"result": validation_result["result"]}), 200
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else:
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return jsonify({"error": "Invalid file", "message": "File format not supported."}), 400
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except Exception as e:
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return jsonify({"error": "Internal Server Error", "message": str(e)}), 500
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@@ -0,0 +1,7 @@
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from pydantic import BaseModel
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from typing import List, Dict
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class ValidateWorker(BaseModel):
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result:str
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@@ -0,0 +1,18 @@
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def validate_worker_prompt() -> str:
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return """
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You are a worker in the company "Validate" where you are asked a specific yes or no question:
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The worker's response: "Yes"
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Since the worker answered "Yes," they are required to submit a document to justify their answer.
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Your role is to analyze the question being asked, review the document the worker uploaded, and verify whether the worker's answer is valid or not.
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The goal is to assess the document's content to see if it aligns with the worker's claim of "Yes."
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response format
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if not validated return "not validated"
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{
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result:"validated"
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}
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"""
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@@ -0,0 +1,66 @@
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import os
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import json
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from openai import OpenAI
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from pydantic import BaseModel, Field
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from typing import List, Dict, Optional
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from src.prompts.sops import *
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from src.prompts.chatbot import *
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from src.models.sop_response_schemas import *
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from src.models.bot_response_schema import *
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from dotenv import load_dotenv
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load_dotenv()
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#SopGeneratorDocument
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class Chatbot:
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def __init__(self):
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self.api_key = os.getenv("OPENAI_API_KEY")
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self.client = OpenAI(api_key=self.api_key)
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self.model = "gpt-4o-mini"
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def _extract_text_from_docs(self, docs):
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"""Extract text content from document objects."""
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return [doc.page_content for doc in docs]
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# Existing methods...
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def validate_worker(self, question, docs) -> VisionMissionResponse:
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"""
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This method is responsible for validating the worker's response ("yes" or "no") using the provided document(s).
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The system generates a prompt, extracts document content, and validates the response using the GPT-4 model.
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:param question: The yes/no question asked to the worker.
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:param docs: A list of document(s) uploaded by the worker.
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:return: VisionMissionResponse containing the validated result or None if an error occurs.
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"""
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try:
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docs_text = self._extract_text_from_docs(docs)
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prompt = validate_worker_prompt()
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response = self.client.beta.chat.completions.parse(
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model=self.model,
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messages=[
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{
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"role": "system",
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"content": f'''{prompt} The document is uploaded next.'''
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},
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{
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"role": "user",
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"content": f"Question :{question}",
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},
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{
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"role": "user",
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"content": [{"type": "text", "text": text} for text in docs_text],
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}
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],
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response_format=ValidateWorker,
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max_tokens=4096,
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temperature=0.1
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)
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# Parse the response from the LLM
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extracted_text = json.loads(response.choices[0].message.content)
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return extracted_text
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except Exception as e:
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print(f"An error occurred: {e}")
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return None
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@@ -1,72 +1,16 @@
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import json
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from src.services.questions_generator import QuestionsGenerator
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# Example usage of the Chatbot class:
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from src.services.chatbot import Chatbot
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from src.utils.document_loader import load_document
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if __name__ == "__main__":
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chatbot = Chatbot()
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# Mocking the SOPs input as described in your example
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mock_sops_input = {
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"sops": {
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"departments": [
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{
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"name": "Accounts Receivable",
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"workers": [
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{
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"name": "Angela Lewis",
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"position": "AR Director",
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"sops": {
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"must": [],
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"shall": [],
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"will": []
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}
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},
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{
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"name": "Jamie Vega",
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"position": "AR Supervisor",
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"sops": {
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"must": [
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"Provide escalation information to the AR Director if additional escalations are needed."
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],
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"shall": [],
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"will": []
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}
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},
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{
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"name": "Jessica Merzougui",
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"position": "AR Supervisor",
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"sops": {
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"must": [
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"Provide escalation information to the AR Director if additional escalations are needed."
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],
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"shall": [],
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"will": []
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}
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},
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{
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"name": "Deniece Santos",
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"position": "AR Analyst",
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"sops": {
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"must": [],
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"shall": [],
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"will": []
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}
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}
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]
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}
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]
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}
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}
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# Example inputs
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path = r"C:\Users\User\Desktop\Blessing_AI\MKD\test_erp_ai\erp_ai\test\erp_ai\data\raw\coding_task_completion_document.pdf"
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# Defining the full input data including additional parameters
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input_data = {
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"sops": json.dumps(mock_sops_input), # Convert SOPs to JSON string format
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"assessment_type": "progress", # Set the assessment type
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"frequency_type": "weekly", # Set the frequency type (daily, weekly, etc.)
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"duration": 4 # Set the duration (number of frequency cycles, e.g., 4 weeks)
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}
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question = "Have you completed Task X?"
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user_input = "Yes"
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docs = load_document(path)
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# Initialize the QuestionsGenerator
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generator = QuestionsGenerator()
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# Invoke the generate_questions method with the mock input
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questions_response = generator.generate_questions(input_data)
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# Print the generated questions response
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print(json.dumps(questions_response, indent=4))
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# Validate the worker's answer using the provided document
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validation_result = chatbot.validate_worker(question, docs)
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print(validation_result)
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