app.py

import re
import joblib
from fastapi import FastAPI, Request
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware.
from sentence_transformers import SentenceTransformer, util

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Or restrict to your domain
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load model and vectorizer
model = joblib.load("team_classifier_model.joblib") 
vectorizer = joblib.load("tfidf_vectorizer.joblib")
sbert_model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2")


def clean_text(text):
    text = re.sub(r"\s+", " ", str(text))
    text = re.sub(r"[^\w\s]", "", text)
    return text.lower().strip()


class InputText(BaseModel):
    subject: str
    message: str


class SimilarityRequest(BaseModel):
    text1: str
    text2: str


@app.get("/")
def root():
    return {"status": "running", "message": "Use POST /classify"}


@app.post("/classify")
async def classify_ticket(data: InputText):
    combined = clean_text(f"{data.subject} {data.message}")
    vec = vectorizer.transform([combined])
    prediction = model.predict(vec)[0]
    return {"team": prediction}


@app.post("/similarity")
async def compute_similarity(data: SimilarityRequest):
    emb1 = sbert_model.encode(data.text1, convert_to_tensor=True)
    emb2 = sbert_model.encode(data.text2, convert_to_tensor=True)
    score = util.pytorch_cos_sim(emb1, emb2).item()
    return {"similarity": score}