Delete app.py

app.py

@@ -1,143 +0,0 @@
-import re
-import numpy as np
-from typing import Dict
-
-import gradio as gr
-from fastapi import FastAPI
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-
-# -------------------------
-# CONFIG
-# -------------------------
-
-MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
-CONFIDENCE_THRESHOLD = 0.35
-
-# -------------------------
-# LOAD MODEL
-# -------------------------
-
-embedder = SentenceTransformer(MODEL_NAME)
-
-# -------------------------
-# RULE-BASED ROUTER
-# -------------------------
-
-GREETINGS = {
-    "hi", "hello", "hey", "yo", "sup", "hola", "hii", "hai"
-}
-
-IMAGE_KEYWORDS = {
-    "draw", "image", "picture", "photo", "generate image", "create image"
-}
-
-CODE_KEYWORDS = {
-    "code", "python", "javascript", "bug", "error", "compile", "program"
-}
-
-def rule_based_route(text: str):
-    t = text.lower().strip()
-
-    if t in GREETINGS:
-        return "chat"
-
-    if any(k in t for k in IMAGE_KEYWORDS):
-        return "image_generation"
-
-    if any(k in t for k in CODE_KEYWORDS):
-        return "code"
-
-    return None  # fallback to semantic router
-
-# -------------------------
-# SEMANTIC INTENTS
-# -------------------------
-
-INTENTS: Dict[str, str] = {
-    "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, planning, or creating a project",
-    "action": "asking the system to perform an action"
-}
-
-INTENT_NAMES = list(INTENTS.keys())
-INTENT_EMBEDDINGS = embedder.encode(list(INTENTS.values()), normalize_embeddings=True)
-
-# -------------------------
-# SEMANTIC ROUTER
-# -------------------------
-
-def semantic_route(text: str):
-    text_emb = embedder.encode([text], normalize_embeddings=True)
-    sims = cosine_similarity(text_emb, INTENT_EMBEDDINGS)[0]
-
-    scores = dict(zip(INTENT_NAMES, sims))
-    top_intent = max(scores, key=scores.get)
-    confidence = scores[top_intent]
-
-    if confidence < CONFIDENCE_THRESHOLD:
-        return "chat", scores  # safe fallback
-
-    return top_intent, scores
-
-# -------------------------
-# MAIN ROUTER
-# -------------------------
-
-def route_intent(text: str):
-    # 1️⃣ Rule-based (instant)
-    rule = rule_based_route(text)
-    if rule:
-        return {
-            "text": text,
-            "top_intent": rule,
-            "method": "rule",
-            "scores": {rule: 1.0}
-        }
-
-    # 2️⃣ Semantic
-    intent, scores = semantic_route(text)
-    return {
-        "text": text,
-        "top_intent": intent,
-        "method": "semantic",
-        "scores": scores
-    }
-
-# -------------------------
-# FASTAPI
-# -------------------------
-
-api = FastAPI()
-
-@api.post("/classify")
-def classify(payload: Dict):
-    text = payload.get("text", "")
-    return route_intent(text)
-
-# -------------------------
-# GRADIO UI
-# -------------------------
-
-def gradio_classify(text):
-    result = route_intent(text)
-    return result
-
-gradio_ui = gr.Interface(
-    fn=gradio_classify,
-    inputs=gr.Textbox(label="User Input"),
-    outputs=gr.JSON(label="Classification Result"),
-    title="🧠 Hybrid Intent Router",
-    description="Rule-based + Semantic intent classification for prompt routing & MPC selection"
-)
-
-# -------------------------
-# MOUNT GRADIO INTO FASTAPI
-# -------------------------
-
-app = gr.mount_gradio_app(api, gradio_ui, path="/")