krishnamishra8848/Nepal_Vehicle_License_Plates_Character_Recognisation

# Example Code: Try this code on google colab

# Step 1: Install Required Libraries
!pip install huggingface_hub

# Step 2: Import Libraries
import os
import numpy as np
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from google.colab import files

# Step 3: Download the Model from Hugging Face
repo_id = "krishnamishra8848/Nepal_Vehicle_License_Plates_Character_Recognisation"
filename = "model.h5"

model_path = hf_hub_download(repo_id=repo_id, filename=filename)

# Step 4: Load the Model
model = load_model(model_path)

# Step 5: Allow User to Upload an Image
uploaded = files.upload()

# Assuming only one file is uploaded
for image_name in uploaded.keys():
    # Step 6: Process the Uploaded Image
    img_height, img_width = 64, 64  # Image dimensions used during training
    img = load_img(image_name, target_size=(img_height, img_width))
    img_array = img_to_array(img)
    img_array = img_array / 255.0  # Normalize pixel values
    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension

    # Step 7: Predict Using the Model
    prediction = model.predict(img_array)
    predicted_class_index = np.argmax(prediction)
    confidence = np.max(prediction)

    # Step 8: Map Index to Class Labels
    class_labels = [
        'क', 'को', 'ख', 'ग', 'च', 'ज', 'झ', 'ञ', 'डि', 'त', 'ना', 'प',
        'प्र', 'ब', 'बा', 'भे', 'म', 'मे', 'य', 'लु', 'सी', 'सु', 'से', 'ह',
        '०', '१', '२', '३', '४', '५', '६', '७', '८', '९'
    ]
    predicted_class = class_labels[predicted_class_index]

    # Step 9: Display the Result
    print(f"Predicted Class: {predicted_class}")
    print(f"Confidence: {confidence:.4f}")