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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() |