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c606906 d0c9791 c606906 39d99af c606906 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import os
import torch
import requests
from io import BytesIO
from PIL import Image
from flask import Flask, request, jsonify
from flask_cors import CORS
import cloudinary
import cloudinary.uploader
from transformers import AutoImageProcessor, AutoModelForImageClassification
app = Flask(__name__)
CORS(app)
cloudinary.config(
cloud_name = "dpf9ahkft",
api_key = "715742843611293",
api_secret = "w7D3JKykv_PVaFRD84DRP_56hIM",
secure = True
)
MODEL_NAME = "prithivMLmods/Realistic-Gender-Classification"
model = AutoModelForImageClassification.from_pretrained(
MODEL_NAME,
trust_remote_code=True
)
processor = AutoImageProcessor.from_pretrained(
MODEL_NAME,
trust_remote_code=True
)
@app.route('/classify', methods=['POST'])
def classify():
try:
data = request.get_json()
image_url = data.get('url')
public_id = data.get('public_id')
if not image_url:
return jsonify({"error": "No URL provided"}), 400
response = requests.get(image_url, timeout=10)
img = Image.open(BytesIO(response.content)).convert("RGB")
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=1).squeeze().tolist()
gender = "female" if probs[0] > probs[1] else "male"
if public_id:
try:
cloudinary.uploader.destroy(public_id)
except:
pass
return jsonify({
"gender": gender,
"confidence": max(probs),
"status": "success"
})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':
port = int(os.environ.get("PORT", 7860))
app.run(host='0.0.0.0', port=port) |