app.py

# app.py

import os
import torch
from flask import Flask, request, jsonify, render_template
from flask_cors import CORS
from werkzeug.utils import secure_filename
from ultralytics import YOLO
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

app = Flask(__name__)

# Enable CORS for all routes
CORS(app)

# --- Configuration ---
UPLOAD_FOLDER = 'static/uploads'
MODELS_FOLDER = 'models'
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}

# --- NEW: Load model names from .env file, with fallback defaults ---
MODEL_1_NAME = os.getenv('MODEL_1_NAME', 'best.pt')
MODEL_2_NAME = os.getenv('MODEL_2_NAME', 'tyre_alloy.pt') # New model for Tyre/Alloy

MODEL_1_PATH = os.path.join(MODELS_FOLDER, MODEL_1_NAME)
MODEL_2_PATH = os.path.join(MODELS_FOLDER, MODEL_2_NAME)

app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
os.makedirs(MODELS_FOLDER, exist_ok=True)
os.makedirs('templates', exist_ok=True)

# --- Determine Device ---
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# --- NEW: Load multiple YOLO Models ---
model1, model2 = None, None

# Load Model 1
try:
    if not os.path.exists(MODEL_1_PATH):
        print(f"Warning: Model file not found at {MODEL_1_PATH}")
    else:
        model1 = YOLO(MODEL_1_PATH)
        model1.to(device)
        print(f"Successfully loaded model '{MODEL_1_NAME}' on {device}.")
except Exception as e:
    print(f"Error loading Model 1 ({MODEL_1_NAME}): {e}")

# Load Model 2
try:
    if not os.path.exists(MODEL_2_PATH):
        print(f"Warning: Model file not found at {MODEL_2_PATH}")
    else:
        model2 = YOLO(MODEL_2_PATH)
        model2.to(device)
        print(f"Successfully loaded model '{MODEL_2_NAME}' on {device}.")
except Exception as e:
    print(f"Error loading Model 2 ({MODEL_2_NAME}): {e}")


def allowed_file(filename):
    """Checks if a file's extension is in the ALLOWED_EXTENSIONS set."""
    return '.' in filename and \
           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

def run_inference(model, filepath):
    """Helper function to run inference and format the result."""
    if model is None:
        return None # Return None if the model isn't loaded

    results = model(filepath)
    result = results[0]
    probs = result.probs
    top1_index = probs.top1
    top1_confidence = float(probs.top1conf)
    class_name = model.names[top1_index]
    
    return {
        "class": class_name,
        "confidence": top1_confidence
    }

@app.route('/')
def home():
    """Serve the main HTML page."""
    return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
    """

    Endpoint to receive an image and run classification based on the requested model type.

    """
    # 1. --- File Validation ---
    if 'file' not in request.files:
        return jsonify({"error": "No file part in the request"}), 400
    file = request.files['file']
    if file.filename == '':
        return jsonify({"error": "No selected file"}), 400
    if not file or not allowed_file(file.filename):
        return jsonify({"error": "File type not allowed"}), 400

    # --- NEW: Get the model type from the form data ---
    model_type = request.form.get('model_type', 'model1') # default to model1

    # 2. --- Save the File Temporarily ---
    filename = secure_filename(file.filename)
    filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
    file.save(filepath)

    # 3. --- Perform Inference based on model_type ---
    try:
        if model_type == 'model1':
            if model1 is None:
                return jsonify({"error": f"Model '{MODEL_1_NAME}' is not loaded. Check server logs."}), 500
            prediction = run_inference(model1, filepath)
            return jsonify(prediction)

        elif model_type == 'model2':
            if model2 is None:
                return jsonify({"error": f"Model '{MODEL_2_NAME}' is not loaded. Check server logs."}), 500
            prediction = run_inference(model2, filepath)
            return jsonify(prediction)

        elif model_type == 'combined':
            if model1 is None or model2 is None:
                 return jsonify({"error": "One or more models required for combined mode are not loaded. Check server logs."}), 500
            
            pred1 = run_inference(model1, filepath)
            pred2 = run_inference(model2, filepath)
            
            combined_prediction = {
                "model1_result": pred1,
                "model2_result": pred2
            }
            return jsonify(combined_prediction)

        else:
            return jsonify({"error": "Invalid model type specified"}), 400

    except Exception as e:
        return jsonify({"error": f"An error occurred during inference: {str(e)}"}), 500
    finally:
        # 4. --- Cleanup ---
        if os.path.exists(filepath):
            os.remove(filepath)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860, debug=True)