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import re
import os
import joblib
from fastapi import FastAPI, File, UploadFile
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from sentence_transformers import SentenceTransformer, util
import cv2
import numpy as np
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Or restrict to your domain
allow_methods=["*"],
allow_headers=["*"],
)
os.environ["HF_HOME"] = "/tmp"
os.environ["TRANSFORMERS_CACHE"] = "/tmp"
os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp"
# 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")
gender_list = ['Male', 'Female']
model = cv2.dnn.readNetFromCaffe("gender_deploy.prototxt", "gender_net.caffemodel")
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}
@app.post("/gender")
async def predict_gender(file: UploadFile = File(...)):
try:
contents = await file.read()
npimg = np.frombuffer(contents, np.uint8)
img = cv2.imdecode(npimg, cv2.IMREAD_COLOR)
blob = cv2.dnn.blobFromImage(img, 1.0, (227, 227), (78.426337, 87.768914, 114.895847), swapRB=False)
model.setInput(blob)
gender_preds = model.forward()
gender = gender_list[gender_preds[0].argmax()]
return {"gender": gender}
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500) |