built interface commit

app.py

@@ -1,7 +1,31 @@
 import gradio as gr
+import tensorflow as tf
+import cv2
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load your machine learning model that is trained to recognize car brands
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+
+# 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()