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README.md

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----
-license: mit
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+# Jarvis Intent Classifier
+
+A lightweight CPU-friendly assistant model that detects user intent and returns a response + action command.
+
+**Architecture:** SentenceTransformer (MiniLM-L6-v2) + Logistic Regression (sklearn)  
+**Trained on:** custom OS assistant dataset
+
+### Example
+
+Input:

inference.py

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+import pickle, random
+from sentence_transformers import SentenceTransformer
+
+# Load model once
+with open("intent_model.pkl", "rb") as f:
+    data = pickle.load(f)
+
+clf = data["classifier"]
+id2label = data["id2label"]
+embedder = SentenceTransformer(data["embed_model"])
+intents_meta = data["intents_meta"]
+
+def predict(text):
+    emb = embedder.encode([text])
+    pred = clf.predict(emb)[0]
+    intent = id2label[pred]
+    meta = intents_meta[intent]
+    response = random.choice(meta["responses"])
+    return {
+        "intent": intent,
+        "response": response,
+        "action": meta["action"]
+    }
+
+# Required entrypoint for Hugging Face inference API
+def predict_intent(inputs: str):
+    return predict(inputs)

intent_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f89de5e99c9fb8e4a476d2827b1f577fe494d305759c139bdd66fa4e7ce1377
+size 356963

requirements.txt

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+scikit-learn
+sentence-transformers