Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load the model
+model = tf.keras.models.load_model('guava_disease_cnn.h5')
+
+
+# Preprocessing
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+# Class labels
+class_names = ['0.Anthracnose', '1.Fruit Fly', '2.Healthy Guava']
+
+# Prediction function
+def classify_image(image):
+    """
+    Process and classify the input image.
+    Args:
+        image: Input image in PIL format.
+
+    Returns:
+        Predicted class label.
+    """
+    # Convert to numpy array
+    opencv_image = np.array(image)
+    # Resize and preprocess the image
+    img = cv2.resize(opencv_image, (150, 150))
+    img = np.expand_dims(img, axis=0).astype('float32')  # Expand dimensions
+    img = test_datagen.standardize(img)  # Normalize the image
+    # Predict using the model
+    predictions = model.predict(img)
+    predicted_class = class_names[np.argmax(predictions)]
+    return predicted_class
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil", label="Upload an image"),
+    outputs=gr.Textbox(label="Predicted Disease"),
+    title="Guava Fruit Disease Classification",
+    description=(
+        "This app classifies diseases in Guava fruits using deep learning. "
+        "Upload an image of a papaya to get started."
+    ),
+    examples=[
+        ["example_images/healthy.jpg"],
+        ["example_images/anthracnose.jpg"],
+        ["example_images/brown_spot.jpg"],
+    ],
+    allow_flagging="never",
+)
+
+# Launch the app
+interface.launch()