Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import tensorflow as tf
-import requests
 import cv2
 import numpy as np
 
@@ -10,23 +9,22 @@ tf_model = tf.keras.models.load_model(tf_model_path)
 
 class_labels = ["Normal", "Cataract"]
 
-def predict(inp):
-    # Use the TensorFlow model to predict Normal or Cataract
-    img_array = cv2.cvtColor(np.array(inp), cv2.COLOR_RGB2BGR)
-    img_array = cv2.resize(img_array, (224, 224))
-    img_array = img_array / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
+# Define a Gradio interface
+def classify_image(input_image):
+    # Preprocess the input image
+    input_image = cv2.resize(input_image, (224, 224))  # Resize the image to match the model's input size
+    input_image = np.expand_dims(input_image, axis=0)  # Add batch dimension
+    input_image = input_image / 255.0  # Normalize pixel values (assuming input range [0, 255])
 
-    prediction_tf = tf_model.predict(img_array)
-    label_index = np.argmax(prediction_tf)
-    confidence_tf = float(prediction_tf[0, label_index])
+    # Make predictions using the loaded model
+    predictions = tf_model.predict(input_image)
+    class_index = np.argmax(predictions, axis=1)[0]
+    predicted_class = class_labels[class_index]
 
-    return class_labels[label_index], confidence_tf
+    return predicted_class
 
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(type="pil"),
-    outputs=["label", "number"],
-)
+# Create a Gradio interface
+input_image = gr.inputs.Image(shape=(224, 224, 3))  # Define the input image shape
+output_label = gr.outputs.Label()  # Define the output label
 
-demo.launch()
+gr.Interface(fn=classify_image, inputs=input_image, outputs=output_label).launch()