car identigier

app.py

@@ -1,25 +1,163 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load image captioning model
-captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+# Load a model trained on car brands/makes
+# Using a model from the Hugging Face hub that's good at vehicle classification
+car_classifier = pipeline("image-classification", 
+                         model="dima806/car_brand_classification")
 
-def caption_image(image):
+# Alternative models you can try:
+# - "nickmuchi/vit-finetuned-cars-biased"
+# - "saltacc/anime-ai-detect"
+# - Or use a general object detection + custom logic
+
+def identify_car(image):
+    if image is None:
+        return "Please upload an image of a car"
+    
+    try:
+        # Convert to RGB if necessary
+        if image.mode != 'RGB':
+            image = image.convert('RGB')
+        
+        # Get predictions
+        predictions = car_classifier(image)
+        
+        # Format results
+        result_text = "🚗 Car Identification Results:\n\n"
+        
+        top_5 = predictions[:5]  # Get top 5 predictions
+        for i, pred in enumerate(top_5, 1):
+            label = pred['label'].replace('_', ' ').title()
+            confidence = pred['score']
+            result_text += f"{i}. {label}: {confidence:.2%}\n"
+        
+        # Try to extract additional information
+        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 enhanced_car_analysis(image):
+    """Enhanced version with more detailed analysis"""
     if image is None:
-        return "Please upload an image"
+        return "Please upload an image of a car"
+    
+    try:
+        # Get basic predictions
+        predictions = car_classifier(image)
+        
+        # Create detailed analysis
+        analysis = "🚗 **Detailed Car Analysis**\n\n"
+        
+        # Top prediction
+        top_pred = predictions[0]
+        brand = top_pred['label'].replace('_', ' ').title()
+        confidence = top_pred['score']
+        
+        analysis += f"**Primary Identification:**\n"
+        analysis += f"• Brand: {brand}\n"
+        analysis += f"• Confidence: {confidence:.2%}\n\n"
+        
+        # Top 3 alternatives
+        analysis += "**Alternative Possibilities:**\n"
+        for i, pred in enumerate(predictions[1:4], 2):
+            alt_brand = pred['label'].replace('_', ' ').title()
+            alt_conf = pred['score']
+            analysis += f"{i}. {alt_brand} ({alt_conf:.2%})\n"
+        
+        # Confidence level interpretation
+        if confidence > 0.8:
+            analysis += f"\n✅ **High confidence** - Very likely a {brand}"
+        elif confidence > 0.6:
+            analysis += f"\n⚠️ **Moderate confidence** - Probably a {brand}"
+        else:
+            analysis += f"\n❓ **Low confidence** - Could be a {brand} or similar brand"
+        
+        # Tips for better identification
+        analysis += "\n\n**💡 Tips for better identification:**"
+        analysis += "\n• Use clear, front/side views of the car"
+        analysis += "\n• Ensure good lighting and focus"
+        analysis += "\n• Avoid images with multiple cars"
+        analysis += "\n• Crop close to the car for better accuracy"
+        
+        return analysis
+        
+    except Exception as e:
+        return f"Error processing image: {str(e)}"
+
+# Create a more advanced interface with tabs
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🚗 Car Brand & Model Identifier")
+    gr.Markdown("Upload a photo of a car to identify its brand and model!")
+    
+    with gr.Tab("Quick Identification"):
+        gr.Markdown("### Fast car brand identification")
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(label="Upload Car Photo", type="pil")
+                quick_btn = gr.Button("Identify Car", variant="primary")
+            with gr.Column():
+                quick_output = gr.Textbox(label="Identification Results", lines=8)
     
-    result = captioner(image)
-    return result[0]['generated_text']
-
-demo = gr.Interface(
-    fn=caption_image,
-    inputs=gr.Image(label="Upload Image", type="pil"),
-    outputs=gr.Textbox(label="Generated Caption"),
-    title="Image Captioning",
-    description="Upload an image and AI will generate a caption for it",
-    examples=[
-        ["https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png"]
-    ]
-)
-
-demo.launch()
+    with gr.Tab("Detailed Analysis"):
+        gr.Markdown("### Comprehensive car analysis")
+        with gr.Row():
+            with gr.Column():
+                detail_input = gr.Image(label="Upload Car Photo", type="pil")
+                detail_btn = gr.Button("Analyze Car", variant="primary")
+            with gr.Column():
+                detail_output = gr.Textbox(label="Detailed Analysis", lines=15)
+    
+    with gr.Tab("About"):
+        gr.Markdown("""
+        ## About This Car Identifier
+        
+        **How it works:**
+        - Uses a deep learning model trained on car images
+        - Identifies car brands/makes from photographs
+        - Provides confidence scores for each prediction
+        
+        **Best practices:**
+        - Use clear, well-lit photos
+        - Front or side views work best
+        - Avoid blurry or distant shots
+        - Single car images yield better results
+        
+        **Supported brands include:** Toyota, Honda, Ford, BMW, Mercedes, Audi, 
+        Volkswagen, Nissan, Chevrolet, Hyundai, and many more!
+        """)
+    
+    # Examples section
+    gr.Markdown("### Example Car Images")
+    gr.Examples(
+        examples=[
+            ["https://cdn.pixabay.com/photo/2015/01/19/13/51/car-604019_1280.jpg"],  # Audi
+            ["https://cdn.pixabay.com/photo/2012/11/02/13/02/car-63930_1280.jpg"],   # Ford Mustang
+            ["https://cdn.pixabay.com/photo/2015/09/02/12/25/bmw-918408_1280.jpg"],  # BMW
+            ["https://cdn.pixabay.com/photo/2014/09/07/22/34/car-tire-438467_1280.jpg"]  # Mercedes
+        ],
+        inputs=image_input,
+        outputs=quick_output,
+        fn=identify_car,
+        cache_examples=True
+    )
+    
+    # Connect buttons
+    quick_btn.click(identify_car, inputs=image_input, outputs=quick_output)
+    detail_btn.click(enhanced_car_analysis, inputs=detail_input, outputs=detail_output)
+
+# For better performance on Hugging Face Spaces
+def load_model():
+    # This will cache the model loading
+    return pipeline("image-classification", 
+                   model="dima806/car_brand_classification")
+
+# Pre-load the model when the app starts
+car_classifier = load_model()
+
+if __name__ == "__main__":
+    demo.launch(share=True)