Update app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model('plant_model_v5-beta.h5')
+
+# Define the class names
+class_names = {
+    0: 'Apple___Apple_scab',
+    1: 'Apple___Black_rot',
+    2: 'Apple___Cedar_apple_rust',
+    3: 'Apple___healthy',
+    4: 'Not a plant',
+    5: 'Blueberry___healthy',
+    6: 'Cherry___Powdery_mildew',
+    7: 'Cherry___healthy',
+    8: 'Corn___Cercospora_leaf_spot Gray_leaf_spot',
+    9: 'Corn___Common_rust',
+    10: 'Corn___Northern_Leaf_Blight',
+    11: 'Corn___healthy',
+    12: 'Grape___Black_rot',
+    13: 'Grape___Esca_(Black_Measles)',
+    14: 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
+    15: 'Grape___healthy',
+    16: 'Orange___Haunglongbing_(Citrus_greening)',
+    17: 'Peach___Bacterial_spot',
+    18: 'Peach___healthy',
+    19: 'Pepper,_bell___Bacterial_spot',
+    20: 'Pepper,_bell___healthy',
+    21: 'Potato___Early_blight',
+    22: 'Potato___Late_blight',
+    23: 'Potato___healthy',
+    24: 'Raspberry___healthy',
+    25: 'Soybean___healthy',
+    26: 'Squash___Powdery_mildew',
+    27: 'Strawberry___Leaf_scorch',
+    28: 'Strawberry___healthy',
+    29: 'Tomato___Bacterial_spot',
+    30: 'Tomato___Early_blight',
+    31: 'Tomato___Late_blight',
+    32: 'Tomato___Leaf_Mold',
+    33: 'Tomato___Septoria_leaf_spot',
+    34: 'Tomato___Spider_mites Two-spotted_spider_mite',
+    35: 'Tomato___Target_Spot',
+    36: 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
+    37: 'Tomato___Tomato_mosaic_virus',
+    38: 'Tomato___healthy'
+}
+
+def edge_and_cut(img, threshold1, threshold2):
+    emb_img = img.copy()
+    edges = cv2.Canny(img, threshold1, threshold2)
+    edge_coors = []
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] != 0:
+                edge_coors.append((i, j))
+
+    if len(edge_coors) == 0:
+        return emb_img
+
+    row_min = edge_coors[np.argsort([coor[0] for coor in edge_coors])[0]][0]
+    row_max = edge_coors[np.argsort([coor[0] for coor in edge_coors])[-1]][0]
+    col_min = edge_coors[np.argsort([coor[1] for coor in edge_coors])[0]][1]
+    col_max = edge_coors[np.argsort([coor[1] for coor in edge_coors])[-1]][1]
+    new_img = img[row_min:row_max, col_min:col_max]
+
+    emb_color = np.array([255], dtype=np.uint8)  # Grayscale version of [255, 0, 0]
+    emb_img[row_min-10:row_min+10, col_min:col_max] = emb_color
+    emb_img[row_max-10:row_max+10, col_min:col_max] = emb_color
+    emb_img[row_min:row_max, col_min-10:col_min+10] = emb_color
+    emb_img[row_min:row_max, col_max-10:col_max+10] = emb_color
+
+    return emb_img
+
+
+
+def classify_and_visualize(image):
+    # Preprocess the image
+    img_array = tf.image.resize(image, [256, 256])
+    img_array = tf.expand_dims(img_array, 0) / 255.0
+
+    # Make a prediction
+    prediction = model.predict(img_array)
+    predicted_class = tf.argmax(prediction[0], axis=-1)
+    confidence = np.max(prediction[0])
+
+    if confidence < 0.60:
+        class_name = "The image you uploaded might not be in the dataset. Try making your leaf background white."
+        bounded_image = image
+    else:
+        class_name = class_names[predicted_class.numpy()]
+        bounded_image = edge_and_cut(image, 200, 400)
+
+    return class_name, confidence, bounded_image
+
+
+iface = gr.Interface(
+    fn=classify_and_visualize,
+    inputs="image",
+    outputs=["text", "number", "image"],
+    interpretation="default",
+    examples=[
+        ['examples/grot.jpg'],
+        ['examples/corn.jpg'],
+    ])
+iface.launch()