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
from bs4 import BeautifulSoup

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({"Server": "Working"})

@app.route("/extractimages",methods=["GET"])
def extract_images():
    try:
        src=request.args.get("src")
        response = requests.get(src)
        soup = BeautifulSoup(response.content,'html.parser')
        img_urls=[]

        img_tags = soup.select('div img')
        for img_tag in img_tags:
            img_url = img_tag['src']
            img_urls.append(img_url)
        return json.dumps({"images":img_urls})
    except Exception as e:
        return e

@app.route("/predict", methods=["GET"])
def predict():
    try:
        src = request.args.get("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]

        # 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)