app.py

import gradio as gr
# import tensorflow as tf
# import cv2

# Load your machine learning model that is trained to recognize car brands


# model = tf.keras.models.load_model("model.h5")

# Define the input and output interfaces for the Gradio interface
inputs = gr.inputs.Image()
outputs = gr.outputs.Textbox()

# Define the function that will be called when the user submits an image
def predict(image):
  # Preprocess the image to be compatible with your model
#   image = cv2.resize(image, (224, 224))
#   image = image / 255.0
#   image = image.reshape(1, 224, 224, 3)
  
  # Use the model to make a prediction
  prediction = 'model.predict(image)'
  
  # Return the predicted brand as a string
  return "The brand of this car is: " + str(prediction)

# Create the Gradio interface
interface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title="Car Brand Predictor")

# Display the interface
interface.launch()