Add inference example script

inference_example.py

@@ -0,0 +1,85 @@
+"""
+Multi-Domain Classifier - Inference Example
+Repository: https://huggingface.co/ovinduG/multi-domain-classifier-phi3
+"""
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import json
+
+class MultiDomainClassifier:
+    def __init__(self, model_id="ovinduG/multi-domain-classifier-phi3"):
+        print("Loading model...")
+        
+        # Load base model
+        self.base_model = AutoModelForCausalLM.from_pretrained(
+            "microsoft/Phi-3-mini-4k-instruct",
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+        
+        # Load LoRA adapter
+        self.model = PeftModel.from_pretrained(self.base_model, model_id)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
+        self.model.eval()
+        
+        print("✅ Model loaded!")
+    
+    def predict(self, query: str) -> dict:
+        """Classify a query into domains"""
+        
+        prompt = f"""Classify this query: {query}
+
+Output JSON format:
+{
+  "primary_domain": "domain_name",
+  "primary_confidence": 0.95,
+  "is_multi_domain": true/false,
+  "secondary_domains": [{"domain": "name", "confidence": 0.85}]
+}"""
+        
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+        
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=200,
+                temperature=0.1,
+                do_sample=False,
+                use_cache=False
+            )
+        
+        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Parse JSON from response
+        try:
+            json_str = response.split("Output JSON format:")[-1].strip()
+            result = json.loads(json_str)
+            return result
+        except:
+            return {"error": "Failed to parse response", "raw": response}
+
+
+# Example usage
+if __name__ == "__main__":
+    # Initialize classifier
+    classifier = MultiDomainClassifier()
+    
+    # Example queries
+    queries = [
+        "Write a Python function to calculate factorial",
+        "Build ML model to analyze sales data and create API endpoints",
+        "What is quantum entanglement?",
+        "Create a REST API for healthcare diabetes prediction"
+    ]
+    
+    print("\n" + "="*80)
+    print("CLASSIFICATION EXAMPLES")
+    print("="*80)
+    
+    for query in queries:
+        print(f"\nQuery: {query}")
+        result = classifier.predict(query)
+        print(f"Result: {json.dumps(result, indent=2)}")
+        print("-"*80)