Create inference.py

inference.py

@@ -0,0 +1,39 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+class CritiqueCoreInference:
+    def __init__(self, model_path):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_path).to(self.device)
+        self.model.eval()
+
+    def analyze(self, text):
+        inputs = self.tokenizer(
+            text, 
+            return_tensors="pt", 
+            padding=True, 
+            truncation=True, 
+            max_length=128
+        ).to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+            conf, pred = torch.max(probs, dim=-1)
+
+        result = "POSITIVE" if pred.item() == 1 else "NEGATIVE"
+        return {
+            "text": text,
+            "label": result,
+            "confidence": f"{conf.item() * 100:.2f}%"
+        }
+
+# Usage
+if __name__ == "__main__":
+    # Point this to your unzipped folder
+    engine = CritiqueCoreInference("./CritiqueCore_v1_HF")
+    
+    sample = "The plot was a bit slow, but overall a great experience."
+    prediction = engine.analyze(sample)
+    print(f"Result: {prediction['label']} | Confidence: {prediction['confidence']}")