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| import os | |
| import torch | |
| import numpy as np | |
| import pandas as pd | |
| import gradio as gr | |
| #from google.colab import files | |
| from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import LabelEncoder | |
| class EnhancedHRAssistantModel: | |
| def __init__(self, model_name='bert-large-uncased-whole-word-masking-finetuned-squad'): | |
| # Use GPU if available | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Load more advanced model for better context understanding | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| self.model = AutoModelForQuestionAnswering.from_pretrained(model_name).to(self.device) | |
| # Configure pipeline with improved parameters | |
| self.qa_pipeline = pipeline( | |
| 'question-answering', | |
| model=self.model, | |
| tokenizer=self.tokenizer, | |
| device=0 if torch.cuda.is_available() else -1 | |
| ) | |
| def generate_response(self, question, context): | |
| try: | |
| # Improved response generation | |
| result = self.qa_pipeline({ | |
| 'question': question, | |
| 'context': context | |
| }) | |
| # Enhanced response formatting | |
| confidence = result.get('score', 0) * 100 | |
| answer = result.get('answer', 'I could not find a specific answer.') | |
| # Construct more informative response | |
| if confidence > 50: | |
| formatted_response = f"{answer}\n\n(Confidence: {confidence:.2f}%)" | |
| return formatted_response | |
| else: | |
| return "I'm not certain about the exact details. Please consult with HR directly." | |
| except Exception as e: | |
| return f"Error generating response: {str(e)}" | |
| def extract_key_information(self, context): | |
| """ | |
| Extract key points from the context for additional insights | |
| """ | |
| # Simple keyword-based extraction | |
| key_phrases = [ | |
| 'benefits', 'coverage', 'policy', 'options', | |
| 'available', 'include', 'provide', 'offer' | |
| ] | |
| extracted_points = [] | |
| sentences = context.split('.') | |
| for sentence in sentences: | |
| if any(phrase in sentence.lower() for phrase in key_phrases): | |
| extracted_points.append(sentence.strip()) | |
| return extracted_points[:3] # Return top 3 key points | |
| # Gradio Interface | |
| class HRAssistantInterface: | |
| def __init__(self, model): | |
| self.model = model | |
| def create_interface(self): | |
| def comprehensive_query_handler(question, context): | |
| # Primary response generation | |
| primary_response = self.model.generate_response(question, context) | |
| # Extract additional key information | |
| additional_info = self.model.extract_key_information(context) | |
| # Combine responses | |
| full_response = f"{primary_response}\n\nAdditional Context:\n" | |
| full_response += "\n".join(f"• {point}" for point in additional_info) | |
| return full_response | |
| iface = gr.Interface( | |
| fn=comprehensive_query_handler, | |
| inputs=[ | |
| gr.Textbox(label="HR Question"), | |
| gr.Textbox(label="Full Policy Context", lines=5) | |
| ], | |
| outputs=gr.Textbox(label="Comprehensive HR Assistant Response"), | |
| title="AJ: Advanced HR Policy Assistant", | |
| description="Get detailed insights into your HR policies" | |
| ) | |
| return iface | |
| # Example Usage | |
| def main(): | |
| # Initialize Enhanced HR Assistant Model | |
| hr_model = EnhancedHRAssistantModel() | |
| # Create Gradio Interface | |
| interface = HRAssistantInterface(hr_model) | |
| gradio_app = interface.create_interface() | |
| # Launch the interface | |
| gradio_app.launch(share=True) | |
| if __name__ == "__main__": | |
| main() |