Upload 4 files

app.py

@@ -0,0 +1,137 @@
+import gradio as gr
+import joblib
+import numpy as np
+from PIL import Image
+
+# --- Imports from your training script ---
+import os
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.preprocessing import image
+
+# --- 1. Configuration (from training) ---
+IMG_WIDTH = 224
+IMG_HEIGHT = 224
+
+# --- 2. Load All Models (Run once on startup) ---
+print("Loading all models...")
+
+# Load the SVM and Scaler
+try:
+    svm_model = joblib.load("svm_classifier.pkl")
+    scaler = joblib.load("scaler.pkl")
+    print("SVM and Scaler loaded.")
+except Exception as e:
+    print(f"CRITICAL ERROR: Could not load .pkl files: {e}")
+    # This will stop the app if models are missing
+    raise FileNotFoundError("Could not find svm_classifier.pkl or scaler.pkl")
+
+# Load the ResNet50 feature extractor
+try:
+    feature_extractor = ResNet50(weights='imagenet', 
+                                 include_top=False, 
+                                 pooling='avg', 
+                                 input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
+    print("ResNet50 feature extractor loaded.")
+except Exception as e:
+    print(f"CRITICAL ERROR: Could not load ResNet50: {e}")
+    # This often happens if tensorflow is not installed
+    raise e
+
+print("--- All models loaded successfully! ---")
+
+
+# --- 3. The Corrected Feature Extraction Function ---
+def extract_features(pil_image):
+    """
+    Processes a single PIL image and extracts ResNet50 features,
+    replicating the logic from train_classifier.py.
+    """
+    
+    # 1. Resize the image to match model's expected input (224, 224)
+    # We use PIL's resize, as the input is already a PIL object
+    pil_image_resized = pil_image.resize((IMG_WIDTH, IMG_HEIGHT))
+    
+    # 2. Convert PIL image to NumPy array (shape: 224, 224, 3)
+    img_array = image.img_to_array(pil_image_resized)
+    
+    # 3. Add batch dimension (model expects 1, 224, 224, 3)
+    img_array_expanded = np.expand_dims(img_array, axis=0)
+    
+    # 4. Preprocess the image for ResNet50 (handles color/pixel scaling)
+    img_preprocessed = preprocess_input(img_array_expanded)
+    
+    # 5. Get the feature vector (shape: 1, 2048)
+    features = feature_extractor.predict(img_preprocessed)
+    
+    # 6. Return the flattened 1D feature vector (shape: 2048,)
+    return features.flatten()
+
+
+# --- 4. The Main Prediction Function (Now More Robust) ---
+def predict(input_image):
+    """
+    The main prediction function called by Gradio.
+    """
+    if not input_image:
+        return None # Handle empty input
+
+    # 1. Extract features using the ResNet50 function
+    try:
+        # features_1d will have shape (2048,)
+        features_1d = extract_features(input_image)
+    except Exception as e:
+        print(f"Error extracting features: {e}")
+        # gr.Error shows a clean error message in the UI
+        raise gr.Error(f"Feature Extraction Failed: {e}")
+
+    # 2. Reshape to 2D for the scaler (shape 1, 2048)
+    features_2d = features_1d.reshape(1, -1)
+    
+    # Check shape just in case
+    if features_2d.shape[1] != scaler.n_features_in_:
+         raise gr.Error(
+            f"Feature Mismatch! Model expects {scaler.n_features_in_} features, "
+            f"but got {features_2d.shape[1]}."
+        )
+
+    # 3. Scale the features
+    try:
+        scaled_features = scaler.transform(features_2d)
+    except Exception as e:
+        print(f"Error scaling features: {e}")
+        raise gr.Error(f"Feature Scaling Failed: {e}")
+
+    # 4. Predict probabilities
+    try:
+        # Ensure your SVM was trained with probability=True
+        probabilities = svm_model.predict_proba(scaled_features)[0]
+        class_labels = svm_model.classes_
+        
+        # Create a {label: probability} dictionary
+        confidences = {label: float(prob) for label, prob in zip(class_labels, probabilities)}
+        return confidences
+        
+    except AttributeError:
+        # Fallback if probability=False
+        prediction = svm_model.predict(scaled_features)[0]
+        return {str(prediction): 1.0} # Return definite prediction
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+        raise gr.Error(f"Prediction Failed: {e}")
+
+
+# --- 5. Create and Launch the Gradio Interface ---
+image_input = gr.Image(type="pil", label="Upload Otolith Image")
+label_output = gr.Label(num_top_classes=3, label="Classification Results")
+
+app = gr.Interface(
+    fn=predict,
+    inputs=image_input,
+    outputs=label_output,
+    title="Otolith Classification Engine",
+    description="Upload an image of an otolith to classify it. This app uses a ResNet50 feature extractor and an SVM classifier."
+)
+
+if __name__ == "__main__":
+    app.launch()

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f41b7222c4c4ff58ef935b47784c849f8c8b39197caac51ec2c774fc56a27e4d
+size 49767

svm_classifier.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb5a45a2335d36f0ee3b453a60dbbcf80b2f1d50ab2769d9b8035f7dfe7e5430
+size 4674815