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app.py

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+import gradio as gr
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+
+# EuroSAT class names (10 land cover classes)
+EUROSAT_CLASSES = [
+    "AnnualCrop",
+    "Forest", 
+    "HerbaceousVegetation",
+    "Highway",
+    "Industrial",
+    "Pasture",
+    "PermanentCrop",
+    "Residential",
+    "River",
+    "SeaLake"
+]
+
+# Class descriptions for better user understanding
+CLASS_DESCRIPTIONS = {
+    "AnnualCrop": "🌾 Agricultural land with annual crops",
+    "Forest": "🌲 Dense forest areas with trees",
+    "HerbaceousVegetation": "🌿 Areas with herbaceous vegetation",
+    "Highway": "🛣️ Major roads and highway infrastructure",
+    "Industrial": "🏭 Industrial areas and facilities",
+    "Pasture": "🐄 Pasture land for livestock",
+    "PermanentCrop": "🍇 Permanent crop areas (vineyards, orchards)",
+    "Residential": "🏘️ Residential areas and neighborhoods",
+    "River": "🏞️ Rivers and waterways",
+    "SeaLake": "🏔️ Seas, lakes, and large water bodies"
+}
+
+class EuroSATClassifier:
+    def __init__(self, model_name="Adilbai/EuroSAT-Swin"):
+        self.model_name = model_name
+        self.processor = None
+        self.model = None
+        self.load_model()
+    
+    def load_model(self):
+        """Load the model and processor"""
+        try:
+            self.processor = AutoImageProcessor.from_pretrained(self.model_name)
+            self.model = AutoModelForImageClassification.from_pretrained(self.model_name)
+            self.model.eval()
+            print(f"✅ Model {self.model_name} loaded successfully!")
+        except Exception as e:
+            print(f"❌ Error loading model: {e}")
+            # Fallback to a generic model if the specific one fails
+            self.processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
+            self.model = AutoModelForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
+    
+    def predict(self, image):
+        """Make prediction on the input image"""
+        if image is None:
+            return None, None, "Please upload an image first!"
+        
+        try:
+            # Preprocess the image
+            inputs = self.processor(images=image, return_tensors="pt")
+            
+            # Make prediction
+            with torch.no_grad():
+                outputs = self.model(**inputs)
+                predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+            
+            # Get top predictions
+            probabilities = predictions[0].numpy()
+            
+            # Create results dictionary
+            results = {}
+            for i, class_name in enumerate(EUROSAT_CLASSES):
+                if i < len(probabilities):
+                    results[class_name] = float(probabilities[i])
+                else:
+                    results[class_name] = 0.0
+            
+            # Sort by confidence
+            sorted_results = dict(sorted(results.items(), key=lambda x: x[1], reverse=True))
+            
+            # Get top prediction
+            top_class = list(sorted_results.keys())[0]
+            top_confidence = list(sorted_results.values())[0]
+            
+            # Create confidence plot
+            confidence_plot = self.create_confidence_plot(sorted_results)
+            
+            # Format result text
+            result_text = f"🎯 **Prediction: {top_class}**\n\n"
+            result_text += f"📊 **Confidence: {top_confidence:.1%}**\n\n"
+            result_text += f"📝 **Description: {CLASS_DESCRIPTIONS.get(top_class, 'Land cover classification')}**\n\n"
+            result_text += "### Top 3 Predictions:\n"
+            
+            for i, (class_name, confidence) in enumerate(list(sorted_results.items())[:3]):
+                result_text += f"{i+1}. **{class_name}**: {confidence:.1%}\n"
+            
+            return sorted_results, confidence_plot, result_text
+            
+        except Exception as e:
+            error_msg = f"❌ Error during prediction: {str(e)}"
+            return None, None, error_msg
+    
+    def create_confidence_plot(self, results):
+        """Create a confidence plot using Plotly"""
+        classes = list(results.keys())
+        confidences = [results[cls] * 100 for cls in classes]
+        
+        # Create color scale - top prediction in green, others in blue gradient
+        colors = ['#2E8B57' if i == 0 else f'rgba(70, 130, 180, {0.8 - i*0.1})' for i in range(len(classes))]
+        
+        fig = go.Figure(data=[
+            go.Bar(
+                x=confidences,
+                y=classes,
+                orientation='h',
+                marker_color=colors,
+                text=[f'{conf:.1f}%' for conf in confidences],
+                textposition='inside',
+                textfont=dict(color='white', size=12),
+            )
+        ])
+        
+        fig.update_layout(
+            title={
+                'text': "🎯 Classification Confidence Scores",
+                'x': 0.5,
+                'xanchor': 'center',
+                'font': {'size': 16, 'color': '#2C3E50'}
+            },
+            xaxis_title="Confidence (%)",
+            yaxis_title="Land Cover Classes",
+            height=500,
+            margin=dict(l=10, r=10, t=50, b=10),
+            plot_bgcolor='rgba(248, 249, 250, 0.8)',
+            paper_bgcolor='white',
+            font=dict(family="Arial, sans-serif", size=12, color="#2C3E50"),
+            xaxis=dict(
+                gridcolor='rgba(128, 128, 128, 0.2)',
+                showgrid=True,
+                range=[0, 100]
+            ),
+            yaxis=dict(
+                gridcolor='rgba(128, 128, 128, 0.2)',
+                showgrid=True,
+                autorange="reversed"  # Show highest confidence at top
+            )
+        )
+        
+        return fig
+
+# Initialize the classifier
+classifier = EuroSATClassifier()
+
+def classify_image(image):
+    """Main classification function for Gradio interface"""
+    return classifier.predict(image)
+
+def get_sample_images():
+    """Return some sample image descriptions"""
+    return """
+    ### 🖼️ Try these types of satellite images:
+    
+    - **🌾 Agricultural fields** - Crop lands and farmland
+    - **🌲 Forest areas** - Dense tree coverage
+    - **🏘️ Residential zones** - Urban neighborhoods  
+    - **🏭 Industrial sites** - Factories and industrial areas
+    - **🛣️ Highway systems** - Major roads and intersections
+    - **💧 Water bodies** - Rivers, lakes, and seas
+    - **🌿 Natural vegetation** - Grasslands and natural areas
+    
+    Upload a satellite/aerial image to see the land cover classification!
+    """
+
+# Custom CSS for better styling
+custom_css = """
+.gradio-container {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}
+
+.main-header {
+    text-align: center;
+    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    padding: 2rem;
+    border-radius: 10px;
+    margin-bottom: 2rem;
+}
+
+.upload-area {
+    border: 2px dashed #667eea;
+    border-radius: 10px;
+    padding: 2rem;
+    text-align: center;
+    background: rgba(102, 126, 234, 0.05);
+}
+
+.result-text {
+    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+    padding: 1.5rem;
+    border-radius: 10px;
+    border-left: 4px solid #667eea;
+}
+"""
+
+# Create the Gradio interface
+with gr.Blocks(css=custom_css, title="🛰️ EuroSAT Land Cover Classifier") as demo:
+    gr.HTML("""
+        <div class="main-header">
+            <h1>🛰️ EuroSAT Land Cover Classifier</h1>
+            <p>Advanced satellite image classification using Swin Transformer</p>
+            <p><strong>Model:</strong> Adilbai/EuroSAT-Swin | <strong>Dataset:</strong> EuroSAT (10 land cover classes)</p>
+        </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.HTML("<h3>📤 Upload Satellite Image</h3>")
+            image_input = gr.Image(
+                label="Upload a satellite/aerial image",
+                type="pil",
+                height=400,
+                elem_classes="upload-area"
+            )
+            
+            classify_btn = gr.Button(
+                "🔍 Classify Land Cover", 
+                variant="primary",
+                size="lg"
+            )
+            
+            gr.HTML("<div style='margin-top: 2rem;'>")
+            gr.Markdown(get_sample_images())
+            gr.HTML("</div>")
+        
+        with gr.Column(scale=1):
+            gr.HTML("<h3>📊 Classification Results</h3>")
+            
+            result_text = gr.Markdown(
+                value="Upload an image and click 'Classify Land Cover' to see results!",
+                elem_classes="result-text"
+            )
+            
+            confidence_plot = gr.Plot(
+                label="Confidence Scores",
+                height=500
+            )
+    
+    # Hidden component to store raw results
+    raw_results = gr.JSON(visible=False)
+    
+    # Event handlers
+    classify_btn.click(
+        fn=classify_image,
+        inputs=[image_input],
+        outputs=[raw_results, confidence_plot, result_text]
+    )
+    
+    # Also trigger on image upload
+    image_input.change(
+        fn=classify_image,
+        inputs=[image_input],
+        outputs=[raw_results, confidence_plot, result_text]
+    )
+    
+    # Footer
+    gr.HTML("""
+        <div style="text-align: center; margin-top: 3rem; padding: 2rem; background: #f8f9fa; border-radius: 10px;">
+            <h4>🔬 About This Model</h4>
+            <p>This classifier uses the <strong>Swin Transformer</strong> architecture trained on the <strong>EuroSAT dataset</strong>.</p>
+            <p>The EuroSAT dataset contains <strong>27,000 satellite images</strong> from <strong>34 European countries</strong>, covering <strong>10 different land cover classes</strong>.</p>
+            <p>Perfect for environmental monitoring, urban planning, and agricultural analysis! 🌍</p>
+            <br>
+            <p><strong>Model:</strong> <a href="https://huggingface.co/Adilbai/EuroSAT-Swin" target="_blank">Adilbai/EuroSAT-Swin</a></p>
+        </div>
+    """)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True
+    )

requirements.txt

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+gradio>=4.0.0
+torch>=1.9.0
+transformers>=4.21.0
+Pillow>=8.3.0
+numpy>=1.21.0
+plotly>=5.0.0