first commit

README.md

@@ -10,4 +10,18 @@ pinned: false
 license: mit
 ---
 
+# Jarvis Intent Classifier (Gradio Space)
+
+This Space runs a lightweight CPU-friendly intent detection assistant.
+
+Input examples:
+- "increase volume"
+- "open gmail"
+- "restart pc"
+
+Output includes predicted intent, response text, and system action.
+
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+

app.py

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+import gradio as gr
+import pickle, random
+from sentence_transformers import SentenceTransformer
+
+# Load model (must be in the same folder)
+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_intent(user_input):
+    """Predict intent and return formatted response."""
+    if not user_input.strip():
+        return "Please enter a command."
+    emb = embedder.encode([user_input])
+    pred = clf.predict(emb)[0]
+    intent = id2label[pred]
+    meta = intents_meta[intent]
+    response = random.choice(meta["responses"])
+    action = meta["action"]
+    return f"🧠 Intent: {intent}\n💬 Response: {response}\n⚙️ Action: {action}"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict_intent,
+    inputs=gr.Textbox(label="Enter your command", placeholder="e.g. restart pc, open gmail"),
+    outputs=gr.Textbox(label="Jarvis Response"),
+    title="🧠 Jarvis Intent Classifier",
+    description="Lightweight intent classification model that detects system commands and returns appropriate responses & actions."
+)
+
+if __name__ == "__main__":
+    demo.launch()

intent_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f89de5e99c9fb8e4a476d2827b1f577fe494d305759c139bdd66fa4e7ce1377
+size 356963

requirements.txt

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+gradio
+sentence-transformers
+scikit-learn