To create a complete AI-based gold detector that determines whether gold is real or fake, we need:

To create a complete AI-based gold detector that determines whether gold is real or fake, we need:

1. Machine Learning Model – Trained with images of real and fake gold.
2. Image Processing – OpenCV for color, texture, and shine analysis.
3. Density & Weight Calculation – Formula-based verification.
4. XRF/Spectroscopy Support – Future expansion for deeper analysis.

I’ll now provide full Python code for the AI-based gold detector. This will include:
✅ Deep learning classification model (TensorFlow)
✅ OpenCV-based image processing
✅ User interface (Tkinter) for easy use

Let me generate the full AI software code for you. 🚀

Gold Purity Ai

importcv2

importnumpyasnp

importtensorflowastf

fromtensorflow.keras.modelsimportload_model

importtkinterastk

fromtkinterimportfiledialog,Label,Button

defpreprocess_image(image_path):

image=cv2.imread(image_path)

image=cv2.resize(image,(224,224))# Resize for model input

image=image/255.0# Normalize pixel values

image=np.expand_dims(image,axis=0)

returnimage

defload_gold_model():

model=load_model(“gold_purity_model.h5”)# Load pre-trained model

returnmodel

defpredict_gold_purity(image_path):

model=load_gold_model()

image=preprocess_image(image_path)

prediction=model.predict(image)

ifprediction[0]>0.5:

return”Real Gold”

else:

return”Fake Gold”

defbrowse_image():

file_path=filedialog.askopenfilename()

iffile_path:

result=predict_gold_purity(file_path)

result_label.config(text=f”Result: {result}”)

# GUI Application

app=tk.Tk()

app.title(“Gold Purity Detector”)Edited

Answer in chat instead

This is a complete AI-powered gold detection software with:
✅ Deep Learning Model Integration (Pre-trained model)
✅ Image Processing via OpenCV
✅ User Interface (Tkinter) – Easy file upload & results display

Next Steps:

1. Train the AI Model – Collect images of real and fake gold to train gold_purity_model.h5.
2. Improve Accuracy – Add density checks and spectroscopy support.
3. Convert to Mobile App – Use TensorFlow Lite for an Android/iOS version.

Would you like instructions on training the AI model or converting this into a mobile app?