Update app.py

app.py

@@ -2,43 +2,37 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 import gradio as gr
-import os
 
-# Load the model
-model = tf.keras.models.load_model("car_brand_classifier_final.h5", compile=False)
-
-# Define image directory where car brand images are stored
-IMAGE_DIR = "car_brands"  # Make sure this folder exists with images named as class labels
+# Load the Keras model
+model = tf.keras.models.load_model("car_brand_classifier_final.h5")
 
 # Define the preprocessing function
-def preprocess_image(image_path):
-    image = Image.open(image_path)  # Open image from path
-    image = image.resize((299, 299))  # Resize to model input size
+def preprocess_image(image):
+    image = image.resize((299, 299))  # Resize to match model input size
     image = np.array(image) / 255.0  # Normalize pixel values
     image = np.expand_dims(image, axis=0)  # Add batch dimension
     return image
 
-# Prediction function
+# Define the prediction function
 def predict(image):
+    # Preprocess the image
     processed_image = preprocess_image(image)
-    predictions = model.predict(processed_image)
-    predicted_class = np.argmax(predictions, axis=1)[0]  # Get predicted class index
 
-    # Find corresponding image for predicted class
-    matching_image_path = os.path.join(IMAGE_DIR, f"{predicted_class}.jpg")
+    # Make a prediction
+    predictions = model.predict(processed_image)
+    predicted_class = np.argmax(predictions, axis=1)[0]
 
-    if os.path.exists(matching_image_path):
-        return matching_image_path  # Return image file path
-    else:
-        return "No matching image found"
+    # Return the result
+    return f"Predicted class: {predicted_class}"
 
-# Gradio Interface
+# Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs="image",
-    outputs="image",  # Change output to "image"
+    outputs="text",
     title="Car Vision",
-    description="Upload an image of a car, and get a matching brand image.",
+    description="Upload an image of a car to classify its brand."
 )
 
+# Launch the app
 iface.launch()