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models/.env

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+# Name of the first model (e.g., your original classifier)
+MODEL_1_NAME=best_88E.pt
+
+# Name of the second model (for Tyre/Alloy classification)
+MODEL_2_NAME=best_TA_377EP.pt

models/app.py

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+# 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)

models/models/best_TA_377EP.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e36d1c8021842cf1b115648bc1ba02e62f43ca888effd7ef56d738f72c6328e
+size 68680503