inference.py

# inference.py - Simple inference script

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import gradio as gr

class IncidentClassifier:
    def __init__(self, model_name="Rajeshwartiwari/incident-classification-model"):
        self.model_name = model_name
        self.tokenizer = None
        self.model = None
        self.id2label = None
        
    def load(self):
        """Load the model and tokenizer"""
        print(f"Loading model: {self.model_name}")
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name)
        self.id2label = self.model.config.id2label
        print(f"Model loaded with {len(self.id2label)} categories")
        return self
    
    def predict(self, text):
        """Make prediction on input text"""
        if not text.strip():
            return "Please enter text", {}
        
        # Tokenize
        inputs = self.tokenizer(
            text,
            return_tensors="pt",
            truncation=True,
            padding=True,
            max_length=128
        )
        
        # Predict
        with torch.no_grad():
            outputs = self.model(**inputs)
            probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
        
        # Get top prediction
        predicted_id = outputs.logits.argmax().item()
        predicted_label = self.id2label.get(predicted_id, "Unknown")
        
        # Get all confidences
        confidences = {}
        for idx, prob in enumerate(probabilities[0]):
            label = self.id2label.get(idx, f"Class_{idx}")
            confidences[label] = float(prob) * 100
        
        return predicted_label, confidences

# Quick test
if __name__ == "__main__":
    classifier = IncidentClassifier().load()
    
    test_cases = [
        "Oracle database connection error ORA-12154",
        "Email not syncing in Outlook",
        "VPN keeps disconnecting every few minutes",
        "Monitor screen flickering issues"
    ]
    
    for test in test_cases:
        label, confidences = classifier.predict(test)
        top_3 = dict(sorted(confidences.items(), key=lambda x: x[1], reverse=True)[:3])
        print(f"\n📝 Input: {test}")
        print(f"   🎯 Predicted: {label}")
        print(f"   📊 Top 3: {top_3}")