add app

app.py

@@ -2,12 +2,25 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 import gradio as gr
+import os
 
 # Load Keras model
 model = tf.keras.models.load_model("car_brand_classifier_final.h5")
 
+# Define the path to the dataset containing car brand folders
+DATASET_PATH = "./Car_Sales_vision_ai_project"
+
+# Get the list of car brands (folder names) in the dataset
+CAR_BRANDS = sorted(os.listdir(DATASET_PATH))
+
+# Ensure the CAR_BRANDS list contains only valid directories
+CAR_BRANDS = [brand for brand in CAR_BRANDS if os.path.isdir(os.path.join(DATASET_PATH, brand))]
+
 # Define the preprocessing function
 def preprocess_image(image):
+    """
+    Preprocess the input image for the model.
+    """
     if isinstance(image, np.ndarray):  # If Gradio gives a NumPy array, convert it to PIL Image
         image = Image.fromarray(image)
 
@@ -19,24 +32,43 @@ def preprocess_image(image):
 
 # Define the prediction function
 def predict(image):
+    """
+    Predict the car brand and return sample images from the corresponding folder.
+    """
     # Preprocess the image
     processed_image = preprocess_image(image)
 
     # Make a prediction
     predictions = model.predict(processed_image)
-    predicted_class = np.argmax(predictions, axis=1)[0]
+    predicted_class_index = np.argmax(predictions, axis=1)[0]
+    predicted_brand = CAR_BRANDS[predicted_class_index]
+
+    # Get sample images from the predicted brand folder
+    brand_folder = os.path.join(DATASET_PATH, predicted_brand)
+    sample_images = []
+    if os.path.exists(brand_folder):
+        for filename in os.listdir(brand_folder)[:5]:  # Limit to 5 sample images
+            img_path = os.path.join(brand_folder, filename)
+            if os.path.isfile(img_path):
+                sample_images.append(Image.open(img_path).resize((200, 200)))  # Resize for consistency
 
-    # Return the result
-    return f"Predicted class: {predicted_class}"
+    # Return the predicted brand and sample images
+    return {
+        "Predicted Brand": predicted_brand,
+        "Sample Images": sample_images or ["No images found for this brand."]
+    }
 
 # Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy"),  # Ensure Gradio passes a NumPy array
-    outputs="text",
+    outputs=[
+        gr.Textbox(label="Predicted Brand"),
+        gr.Gallery(label="Sample Images").style(grid=[5], height="auto")  # Display images in a gallery
+    ],
     title="Car Vision",
-    description="Upload an image of a car to classify its brand."
+    description="Upload an image of a car to classify its brand and view sample images."
 )
 
 # Launch the app
-iface.launch()
+iface.launch()