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import os
import warnings
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
import torch
from flask_cors import CORS
from flask import Flask, request, json, Response
import numpy as np
from PIL import Image
import requests
from io import BytesIO
os.environ["CUDA_VISIBLE_DEVICES"] = ""
app = Flask(__name__)
cors = CORS(app)
# Define the model and feature extractor globally
model = AutoModelForImageClassification.from_pretrained('carbon225/vit-base-patch16-224-hentai')
feature_extractor = AutoFeatureExtractor.from_pretrained('carbon225/vit-base-patch16-224-hentai')
@app.route("/", methods=["GET"])
def default():
return json.dumps({"Hello I am Chitti": "Speed 1 Terra Hertz, Memory 1 Zeta Byte"})
@app.route("/predict", methods=["GET"])
def predict():
try:
src = request.args.get("src")
print(f"{src=}")
# Download image from the provided URL
response = requests.get(src)
response.raise_for_status() # Check for HTTP errors
# Open and preprocess the image
image = Image.open(BytesIO(response.content))
image = image.resize((128, 128))
# Extract features using the pre-trained feature extractor
encoding = feature_extractor(images=image.convert("RGB"), return_tensors="pt")
# Make a prediction using the pre-trained model
with torch.no_grad():
outputs = model(**encoding)
logits = outputs.logits
# Get the predicted class index and label
predicted_class_idx = logits.argmax(-1).item()
predicted_class_label = model.config.id2label[predicted_class_idx]
print(predicted_class_label)
# Return the predictions
return json.dumps({"class": predicted_class_label})
except requests.exceptions.RequestException as e:
return json.dumps({"error": f"Request error: {str(e)}"})
except Exception as e:
return json.dumps({"error": f"An unexpected error occurred: {str(e)}"})
if __name__ == "__main__":
app.run(debug=True)
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