import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface_cache"  # Set cache directory to a writable location

from flask import Flask, request, render_template, jsonify
from transformers import ViTForImageClassification, ViTFeatureExtractor
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import io

app = Flask(__name__)

# Load the ViT model and its feature extractor
model_name = "google/vit-base-patch16-224-in21k"
model = ViTForImageClassification.from_pretrained(model_name)
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)

# Load the trained model weights
num_classes = 7
model.classifier = nn.Linear(model.config.hidden_size, num_classes)
model.load_state_dict(torch.load("skin_cancer_model.pth", map_location=torch.device('cpu')))
model.eval()

# Define class labels
class_labels = ['benign_keratosis-like_lesions', 'basal_cell_carcinoma', 'actinic_keratoses', 'vascular_lesions', 'melanocytic_Nevi', 'melanoma', 'dermatofibroma']

# Define optimal thresholds
thresholds = [0.88134295, 0.43095806, 0.39622146, 0.90647435, 0.8128958, 0.05310565, 0.15926854]

# Define image transformations
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])

@app.route('/')
def index():
    return render_template('index.html', appName="Skin Cancer Classification Application")

def model_predict(image):
    image = transform(image).unsqueeze(0)  # Add batch dimension
    with torch.no_grad():
        outputs = model(image)
    return outputs

@app.route('/predictApi', methods=["POST"])
def api():
    try:
        if 'fileup' not in request.files:
            return jsonify({'Error': "Please try again. The Image doesn't exist"})
        file = request.files.get('fileup')
        image = Image.open(io.BytesIO(file.read()))
        result = model_predict(image)
        
        probabilities = torch.softmax(result.logits, dim=1).cpu().numpy()[0]
        predicted_idx = torch.argmax(torch.tensor(probabilities)).item()
        max_prob = probabilities[predicted_idx]
        threshold = thresholds[predicted_idx]
        
        if max_prob < threshold:
            return jsonify({'Error': 'No cancer detected or benign lesion.'})
        prediction = class_labels[predicted_idx]
        return jsonify({'prediction': prediction})
    except Exception as e:
        return jsonify({'Error': 'An error occurred', 'Message': str(e)})

@app.route('/predict', methods=['GET', 'POST'])
def predict():
    if request.method == 'POST':
        try:
            if 'fileup' not in request.files:
                return render_template('index.html', prediction='No file selected.', appName="Skin Cancer Classification Application")
            file = request.files['fileup']
            image = Image.open(io.BytesIO(file.read()))
            result = model_predict(image)
            
            probabilities = torch.softmax(result.logits, dim=1).cpu().numpy()[0]
            predicted_idx = torch.argmax(torch.tensor(probabilities)).item()
            max_prob = probabilities[predicted_idx]
            threshold = thresholds[predicted_idx]
            
            if max_prob < threshold:
                return render_template('index.html', prediction='No cancer detected or benign lesion.', appName="Skin Cancer Classification Application")
            prediction = class_labels[predicted_idx]
            return render_template('index.html', prediction=prediction, appName="Skin Cancer Classification Application")
        except Exception as e:
            return render_template('index.html', prediction='Error: ' + str(e), appName="Skin Cancer Classification Application")
    else:
        return render_template('index.html', appName="Skin Cancer Classification Application")

if __name__ == '__main__':
    app.run(debug=True)