car identifier

app.py

@@ -1,86 +1,50 @@
 import gradio as gr
 from transformers import pipeline
 from datasets import load_dataset
-from PIL import Image
-import requests
-import io
 
-# Load your custom dataset
+
 def load_my_dataset():
-    try:
-        dataset = load_dataset("Avmromanov/tripoexamples")
-        return dataset
-    except Exception as e:
-        print(f"Error loading dataset: {e}")
-        return None
+    dataset = load_dataset("Avmromanov/tripoexamples")
+    return dataset
+
+def get_dataset_examples(dataset):
+    train_data = dataset['train']
+    example_ids = [0, 3, 6]
+
+    examples = []
+    for i in example_ids:
+        example = train_data[i]
+        examples.append(example['image'])
+    
+    return examples
 
-# Load car classification model
 car_classifier = pipeline("image-classification", 
                          model="dima806/car_models_image_detection")
 
 def identify_car(image):
-    if image is None:
-        return "Please upload an image of a car"
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
     
-    try:
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
-        
-        predictions = car_classifier(image)
-        
-        result_text = "🚗 Car Identification Results:\n\n"
-        top_5 = predictions[:5]
-        
-        for i, pred in enumerate(top_5, 1):
-            label = pred['label'].replace('_', ' ').title()
-            confidence = pred['score']
-            result_text += f"{i}. {label}: {confidence:.2%}\n"
-        
-        result_text += f"\n🔍 Most likely: **{top_5[0]['label'].replace('_', ' ').title()}** " \
-                      f"(confidence: {top_5[0]['score']:.2%})"
-        
-        return result_text
-        
-    except Exception as e:
-        return f"Error processing image: {str(e)}"
-
-def get_dataset_examples(dataset, num_examples=3):
-    """Extract example images from the dataset"""
-    examples = []
+    predictions = car_classifier(image)
     
-    if dataset is None:
-        return examples
+    result_text = "Car Identification Results:\n\n"
+    top_5 = predictions[:5]
     
-    try:
-        # Adjust this based on your dataset structure
-        train_data = dataset['train']
-        
-        for i in range(min(num_examples, len(train_data))):
-            example = train_data[i]
-            
-            # The structure depends on your dataset - adjust accordingly
-            if 'image' in example:
-                # If images are stored in the dataset
-                examples.append(example['image'])
-            elif 'url' in example:
-                # If URLs are provided
-                examples.append(example['url'])
-            elif 'path' in example:
-                # If file paths are provided
-                examples.append(example['path'])
-                
-    except Exception as e:
-        print(f"Error extracting examples: {e}")
+    for i, pred in enumerate(top_5, 1):
+        label = pred['label'].replace('_', ' ').title()
+        confidence = pred['score']
+        result_text += f"{i}. {label}: {confidence:.2%}\n"
     
-    return examples
+    result_text += f"\n🔍 Most likely: **{top_5[0]['label'].replace('_', ' ').title()}** " \
+                    f"(confidence: {top_5[0]['score']:.2%})"
+    
+    return result_text
 
-# Load your dataset
 my_dataset = load_my_dataset()
 dataset_examples = get_dataset_examples(my_dataset, num_examples=4)
 
-# Create the interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🚗 Car Identifier with My Dataset")
+    gr.Markdown("# Car Identifier with My Dataset")
     gr.Markdown("Using examples from: **Avmromanov/tripoexamples**")
     
     with gr.Row():
@@ -90,8 +54,6 @@ with gr.Blocks() as demo:
         with gr.Column():
             output_text = gr.Textbox(label="Results", lines=10)
     
-
-    gr.Markdown(f"### Dataset Examples (showing {len(dataset_examples)} samples)")
     gr.Examples(
         examples=dataset_examples,
         inputs=image_input,
@@ -99,12 +61,5 @@ with gr.Blocks() as demo:
         fn=identify_car,
         cache_examples=True
     )
-    
-    gr.Markdown("""
-    **Dataset Information:**
-    - Name: tripoexamples
-    - Author: Avmromanov
-    - Type: Car images for identification
-    """)
 
-demo.launch()
+demo.launch()