final model

app.py

@@ -2,20 +2,14 @@ import gradio as gr
 import numpy as np
 
 # Load a pre-trained image classification model
-
+learn = load_learner('models/model.pth')
 
 # Function to make predictions from an image
 def classify_image(image):
-    # Resize the image to the required input size of the model
-    
-    # Preprocess the image to match the model's requirements
-    
     # Make a prediction
-    
     # Decode the prediction and get the class name
-    class_name = np.random.randint(0,3)
-    classes = ["Acura", "Audi", "Dodge"]
-    return classes[class_name]
+    name = learn.predict(image)
+    return name[0]
 
 # Sample images for user to choose from
 sample_images = ["AcuraTLType-S2008.jpg", "AudiR8Coupe2012.jpg", "DodgeMagnumWagon2008.jpg"]
@@ -25,7 +19,8 @@ iface = gr.Interface(
     inputs=gr.Image(label="Select an image", type="filepath"),
     outputs="text",
     live=True,
-    title="Identify the car",
+    title="Car image classifier",
+    description="Upload a car image or select one of the examples below"
     examples=sample_images
 )