Update app.py

app.py

@@ -1,56 +1,56 @@
-import os
-import pickle
-import numpy as np
-import tensorflow as tf
-from flask import Flask, render_template, request
-from PIL import Image
-import keras
-from keras.applications.vgg16 import preprocess_input 
-from tensorflow.keras.applications.vgg16 import preprocess_input 
-import gradio as gr
-
-def model(img):
-    model_dir = 'cataract'
-    class_names = ['Normal', 'Cataract']  # Cataract class labels
-    channel='RGB'
-    selected_model = 'cataract'
-
-    architecture_path = os.path.join('models',model_dir, f'model_architecture_{selected_model}.pkl')
-    weights_path = os.path.join('models',model_dir, f'model_weights_{selected_model}.pkl')
-
-    with open(architecture_path, 'rb') as f:
-        loaded_model_architecture = pickle.load(f)
-
-    with open(weights_path, 'rb') as f:
-        loaded_model_weights = pickle.load(f)
-    
-    # Create the model using the loaded architecture
-    loaded_model = tf.keras.models.model_from_json(loaded_model_architecture)
-
-    # Set the loaded weights to the model
-    loaded_model.set_weights(loaded_model_weights)
-
-    # Load and preprocess the image
-    try:
-        image = Image.open(img).convert(channel).resize((256, 256))
-    except:
-        print("ERROR")
-        return "ERROR"
-    x = np.array(image)
-    x = np.expand_dims(x, axis=0)
-    x = preprocess_input(x)
-
-    # Make predictions
-    predictions = loaded_model.predict(x)
-    print(predictions)
-
-    predicted_class_index = 1 if (predictions[0]>0.5) else 0
-    print(predicted_class_index)
-
-    predicted_class_label = class_names[predicted_class_index]
-
-    return predicted_class_label
-
-cataract_app = gr.Interface(model,gr.Image())
-
+import os
+import pickle
+import numpy as np
+import tensorflow as tf
+from flask import Flask, render_template, request
+from PIL import Image
+import keras
+from keras.applications.vgg16 import preprocess_input 
+from tensorflow.keras.applications.vgg16 import preprocess_input 
+import gradio as gr
+
+def model(img):
+    model_dir = 'cataract'
+    class_names = ['Normal', 'Cataract']  # Cataract class labels
+    channel='RGB'
+    selected_model = 'cataract'
+
+    architecture_path = os.path.join('models',model_dir, f'model_architecture_{selected_model}.pkl')
+    weights_path = os.path.join('models',model_dir, f'model_weights_{selected_model}.pkl')
+
+    with open(architecture_path, 'rb') as f:
+        loaded_model_architecture = pickle.load(f)
+
+    with open(weights_path, 'rb') as f:
+        loaded_model_weights = pickle.load(f)
+    
+    # Create the model using the loaded architecture
+    loaded_model = tf.keras.models.model_from_json(loaded_model_architecture)
+
+    # Set the loaded weights to the model
+    loaded_model.set_weights(loaded_model_weights)
+
+    # Load and preprocess the image
+    try:
+        image = Image.open(img).convert(channel).resize((256, 256))
+    except:
+        print("ERROR")
+        return "ERROR"
+    x = np.array(image)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+
+    # Make predictions
+    predictions = loaded_model.predict(x)
+    print(predictions)
+
+    predicted_class_index = 1 if (predictions[0]>0.5) else 0
+    print(predicted_class_index)
+
+    predicted_class_label = class_names[predicted_class_index]
+
+    return predicted_class_label
+
+cataract_app = gr.Interface(model,gr.Image(),["text"])
+
 cataract_app.launch(share=True)