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from flask import Flask, request, jsonify, render_template
import torch
import torchvision.transforms as transforms
from PIL import Image
import torchvision.models as models
import io
import os
app = Flask(__name__)
# Load the trained model
model_path = "smart_recycling_model1.pth"
model = models.resnet50(pretrained=False)
num_ftrs = model.fc.in_features
model.fc = torch.nn.Linear(num_ftrs, 6) # 6 categories
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()
# Define categories
categories = ["cardboard", "glass", "metal", "paper", "plastic", "trash"]
# Define transformation
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return jsonify({'error': 'No file uploaded'}), 400
file = request.files['file']
image = Image.open(io.BytesIO(file.read()))
image = transform(image).unsqueeze(0) # Add batch dimension
with torch.no_grad():
output = model(image)
probabilities = torch.nn.functional.softmax(output[0], dim=0) # Get confidence scores
predicted_idx = torch.argmax(probabilities).item()
confidence = probabilities[predicted_idx].item() * 100
category = categories[predicted_idx]
response = {
'prediction': category,
'confidence': f"{confidence:.2f}%",
'all_probabilities': {categories[i]: f"{probabilities[i].item() * 100:.2f}%" for i in range(len(categories))},
'recycling_guidelines': {
"cardboard": "Recycle in a dry, clean state. Remove any tape or labels.",
"glass": "Rinse and recycle. Avoid broken glass.",
"metal": "Rinse and place in the metal recycling bin.",
"paper": "Keep dry. Do not include wax-coated paper.",
"plastic": "Check recycling code on the item. Rinse before recycling.",
"trash": "Non-recyclable. Dispose of responsibly.",
}[category]
}
return jsonify(response)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000, debug=True) |