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cgi_classification_app.py

@@ -6,12 +6,10 @@ Automatically generated by Colab.
 Original file is located at
     https://colab.research.google.com/drive/1ckzOtXUiFW_NqlIandwoH07lnsLGKTLB
 """
-
-!pip install gradio
-
 from scipy.spatial import distance
 import numpy as np
 
+
 class MeanClassifier:
     def fit(self, X, y):
         self.mean_0 = np.mean(X[y == 0], axis=0) if np.any(y == 0) else None
@@ -20,26 +18,45 @@ class MeanClassifier:
     def predict(self, X):
         preds = []
         for x in X:
-            dist_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf
-            dist_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf
+            dist_0 = (
+                distance.euclidean(x, self.mean_0)
+                if self.mean_0 is not None
+                else np.inf
+            )
+            dist_1 = (
+                distance.euclidean(x, self.mean_1)
+                if self.mean_1 is not None
+                else np.inf
+            )
             preds.append(1 if dist_1 < dist_0 else 0)
         return np.array(preds)
 
     def predict_proba(self, X):
-      # An implementation of probability prediction which uses a softmax function to determine the probability of each class based on the distance to the mean for each prototype
-      preds = []
-      for x in X:
-        dist_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np
-        dist_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf
-        prob_0 = np.exp(-dist_0) / (np.exp(-dist_0) + np.exp(-dist_1))
-        prob_1 = np.exp(-dist_1) / (np.exp(-dist_0) + np.exp(-dist_1))
-        preds.append([prob_0, prob_1])
-      return np.array(preds)
+        # An implementation of probability prediction which uses a softmax function to determine the probability of each class based on the distance to the mean for each prototype
+        preds = []
+        for x in X:
+            dist_0 = (
+                distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np
+            )
+            dist_1 = (
+                distance.euclidean(x, self.mean_1)
+                if self.mean_1 is not None
+                else np.inf
+            )
+            prob_0 = np.exp(-dist_0) / (np.exp(-dist_0) + np.exp(-dist_1))
+            prob_1 = np.exp(-dist_1) / (np.exp(-dist_0) + np.exp(-dist_1))
+            preds.append([prob_0, prob_1])
+        return np.array(preds)
 
     def mean_distance(self, x):
-      dist_mean_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf
-      dist_mean_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf
-      return dist_mean_0, dist_mean_1
+        dist_mean_0 = (
+            distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf
+        )
+        dist_mean_1 = (
+            distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf
+        )
+        return dist_mean_0, dist_mean_1
+
 
 import gradio as gr
 from PIL import Image
@@ -50,8 +67,8 @@ from tensorflow.keras.models import load_model, Model
 import pickle
 
 mean_clf = None
-with open('mean_clf.pkl', 'rb') as f:
-  mean_clf = pickle.load(f)
+with open("mean_clf.pkl", "rb") as f:
+    mean_clf = pickle.load(f)
 
 
 # Function to apply Fourier transform
@@ -60,21 +77,25 @@ def apply_fourier_transform(image):
     fft_image = fft2(image)
     return np.abs(fft_image)
 
+
 def preprocess_image(image):
     try:
-      image = Image.fromarray(image)
-      image = image.convert('L')
-      image = image.resize((256, 256))
-      image = apply_fourier_transform(image)
-      image = np.expand_dims(image, axis=-1)  # Expand dimensions to match model input shape
-      image = np.expand_dims(image, axis=0)   # Expand to add batch dimension
-      return image
+        image = Image.fromarray(image)
+        image = image.convert("L")
+        image = image.resize((256, 256))
+        image = apply_fourier_transform(image)
+        image = np.expand_dims(
+            image, axis=-1
+        )  # Expand dimensions to match model input shape
+        image = np.expand_dims(image, axis=0)  # Expand to add batch dimension
+        return image
     except Exception as e:
         print(f"Error processing image: {e}")
         return None
 
+
 # Function to load embedding model and calculate embeddings
-def calculate_embeddings(image, model_path='embedding_modelv2.keras'):
+def calculate_embeddings(image, model_path="embedding_modelv2.keras"):
     # Load the trained model
     model = load_model(model_path)
 
@@ -91,16 +112,14 @@ def calculate_embeddings(image, model_path='embedding_modelv2.keras'):
 
 def classify_image(image):
     embeddings = calculate_embeddings(image)
-      # Convert to 2D array for model input
+    # Convert to 2D array for model input
     probabilities = mean_clf.predict_proba(embeddings)[0]
     labels = ["Photo", "CGI"]
     return {f"{labels[i]}": prob for i, prob in enumerate(probabilities)}
 
+
 interface = gr.Interface(
-    fn=classify_image,
-    inputs=["image"],
-    outputs=gr.Label(num_top_classes=2)
+    fn=classify_image, inputs=["image"], outputs=gr.Label(num_top_classes=2)
 )
 
 interface.launch(share=True)
-