add application file

app.py

@@ -0,0 +1,109 @@
+import gradio as gr
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import os
+
+# Get model path (Windows compatible)
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# Try different possible filenames
+possible_names = ['ConvNextmodel.pth', 'convnextmodel.pth', 'ConvNext_model.pth']
+model_path = None
+
+for name in possible_names:
+    test_path = os.path.join(BASE_DIR, name)
+    if os.path.exists(test_path):
+        model_path = test_path
+        print(f"✓ Found model: {name}")
+        break
+
+if model_path is None:
+    raise FileNotFoundError(f"Could not find model file. Tried: {possible_names}")
+
+# Species categories (7 classes)
+SPECIES_CATEGORIES = [
+    'Eel',
+    'Scallop',
+    'Crab',
+    'Flatfish',
+    'Roundfish',
+    'Skate',
+    'Whelk'
+]
+
+# Load model
+print(f"Loading model from: {model_path}")
+model = AutoModelForImageClassification.from_pretrained(
+    'facebook/convnext-tiny-224',
+    num_labels=7,
+    ignore_mismatched_sizes=True
+)
+
+# Load weights
+checkpoint = torch.load(model_path, map_location='cpu', weights_only=False)
+if isinstance(checkpoint, dict):
+    if 'model' in checkpoint:
+        checkpoint = checkpoint['model']
+    elif 'state_dict' in checkpoint:
+        checkpoint = checkpoint['state_dict']
+
+model.load_state_dict(checkpoint, strict=False)
+model.eval()
+
+# Load processor
+processor = AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224')
+print("✓ Model loaded successfully!")
+
+def classify_image(image):
+    """
+    Classify a benthic species image.
+    
+    Args:
+        image: PIL Image or numpy array
+    
+    Returns:
+        dict: Predictions with species names and confidence scores
+    """
+    # Convert to PIL if needed
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image).convert('RGB')
+    
+    # Preprocess
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Predict
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.nn.functional.softmax(logits, dim=1)
+    
+    # Create results dictionary for Gradio
+    results = {}
+    for idx, prob in enumerate(probabilities[0]):
+        results[SPECIES_CATEGORIES[idx]] = float(prob)
+    
+    return results
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil", label="Upload Underwater Image"),
+    outputs=gr.Label(num_top_classes=7, label="Species Classification"),
+    title="🌊 BenthicAI - Benthic Species Classifier",
+    description="Upload an image of a benthic organism to classify it into one of 7 species categories. Built with ConvNeXT transformer model.",
+    examples=[
+        [os.path.join("examples", "eel.jpg")],
+        [os.path.join("examples", "scallop.jpg")],
+        [os.path.join("examples", "crab.jpg")],
+    ] if os.path.exists("examples") else None,
+    theme=gr.themes.Soft(),
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True  # Set to True to get a public URL
+    )