Create app.py

app.py

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+import numpy as np
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# -----------------------
+# LOAD MODEL (CPU-safe)
+# -----------------------
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# -----------------------
+# INTENT DEFINITIONS
+# -----------------------
+INTENTS = {
+    "chat": "casual conversation, greetings, talking",
+    "search": "asking for information or facts",
+    "image_generation": "requesting image creation or visual generation",
+    "code": "programming, software development, debugging",
+    "research": "deep technical or academic research",
+    "study": "learning, studying, explanations, tutorials",
+    "project": "building or planning a project",
+    "action": "asking the system to do something"
+}
+
+intent_names = list(INTENTS.keys())
+intent_embeddings = model.encode(
+    list(INTENTS.values()),
+    normalize_embeddings=True
+)
+
+# -----------------------
+# ROUTER
+# -----------------------
+def classify(text: str):
+    text = text.strip()
+    if not text:
+        return {}
+
+    text_emb = model.encode([text], normalize_embeddings=True)
+    sims = cosine_similarity(text_emb, intent_embeddings)[0]
+
+    scores = {k: float(v) for k, v in zip(intent_names, sims)}
+    top_intent = max(scores, key=scores.get)
+
+    return {
+        "text": text,
+        "top_intent": top_intent,
+        "scores": scores
+    }
+
+# -----------------------
+# GRADIO APP (THIS IS IMPORTANT)
+# -----------------------
+app = gr.Interface(
+    fn=classify,
+    inputs=gr.Textbox(label="User Input"),
+    outputs=gr.JSON(label="Classification Result"),
+    title="🧠 Zero-Shot Intent Router",
+    description="Single-pass intent routing for prompt + MPC selection"
+)