Add looped sentiment inference script

sentiment_inference.py

@@ -0,0 +1,27 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the pretrained sentiment model
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Function to classify sentiment
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    prediction = torch.argmax(probs).item()
+    label = "positive" if prediction == 1 else "negative"
+    return label, probs[0][prediction].item()
+
+# Try it out!
+if __name__ == "__main__":
+    print("🧠 Sentiment Analyzer (type 'exit' to quit)\n")
+    while True:
+        sentence = input("Enter a sentence: ").strip()
+        if sentence.lower() in ["exit", "quit"]:
+            print("👋 Goodbye!")
+            break
+        sentiment, confidence = analyze_sentiment(sentence)
+        print(f"🧠 Sentiment: {sentiment.capitalize()} (Confidence: {confidence:.2f})\n")