Create app.py

app.py

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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()