Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2  # Ensure you have opencv-python installed
+from tensorflow.keras.models import load_model  # Ensure you have TensorFlow installed
+
+# Load your trained model
+model = load_model(r"F:\pe udemy\PROJECTS\incomplete\cancer\breast_cancer_detection_model3.h5")  # Update this path to your actual model file
+
+# Define class names according to your model
+class_names = ['benign', 'malignant', 'normal']  # Update this list if different
+
+# Define the prediction function
+def predict_cancer(images):
+    results = []
+    for img in images:
+        # Convert image to grayscale (if it's not already), resize, and normalize
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)  # Convert to grayscale if not already
+        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))  # Resize to match model input
+        img = np.expand_dims(img, axis=-1)  # Add channel dimension
+        img = img / 255.0  # Normalize
+        img = np.expand_dims(img, axis=0)  # Add batch dimension
+
+        # Make prediction
+        prediction = model.predict(img)
+        class_idx = np.argmax(prediction[0])
+        class_name = class_names[class_idx]
+        probability = np.max(prediction[0])
+        results.append(f"{class_name} (Probability: {probability:.2f})")
+
+    return results
+
+# Define Gradio interface
+def classify_images(images):
+    if not isinstance(images, list):  # Ensure `images` is a list of images
+        images = [images]
+    return predict_cancer(images)
+
+# Define the Gradio interface
+input_images = gr.Image(type='numpy', label='Upload Ultrasound Images')
+output_labels = gr.Textbox(label='Predictions')
+
+gr_interface = gr.Interface(
+    fn=classify_images,
+    inputs=input_images,
+    outputs=output_labels,
+    title="Breast Cancer Detection from Ultrasound Images",
+    description="Upload multiple breast ultrasound images to get predictions on whether they show benign, malignant, or normal conditions."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    gr_interface.launch()