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b8acd28 9f5425b 20a0f19 f884788 20a0f19 e6439c1 20a0f19 b8acd28 20a0f19 f884788 e6439c1 f884788 20a0f19 f884788 b8acd28 e6439c1 b8acd28 84f94fd b8acd28 20a0f19 f884788 20a0f19 9f5425b 20a0f19 e6439c1 20a0f19 9f5425b 20a0f19 e6439c1 9f5425b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | # import gradio as gr
# import tensorflow as tf
# import numpy as np
# import json
# from tensorflow.keras.applications.efficientnet import preprocess_input
# from tensorflow.keras.preprocessing import image as keras_image
# # Load Model & Class Indices
# MODEL_PATH = "model.keras"
# CLASS_INDICES_PATH = "class_indices.json"
# FLOWER_INFO_PATH = "flower_info.json"
# def load_model():
# return tf.keras.models.load_model(MODEL_PATH)
# def load_class_indices():
# with open(CLASS_INDICES_PATH, "r") as f:
# return json.load(f)
# def load_flower_info():
# with open(FLOWER_INFO_PATH, "r", encoding="utf-8") as f:
# return json.load(f)
# model = load_model()
# class_indices = load_class_indices()
# flower_info = load_flower_info()
# class_names = list(class_indices.keys())
# def preprocess_image(pil_image):
# # Convert PIL image to numpy array and preprocess
# img_array = keras_image.img_to_array(pil_image.resize((224, 224)))
# img_array = np.expand_dims(img_array, axis=0)
# return preprocess_input(img_array)
# def predict_image(pil_image):
# img_array = preprocess_image(pil_image)
# predictions = model.predict(img_array)
# predicted_class = class_names[np.argmax(predictions[0])]
# info = flower_info.get(predicted_class, "No additional information available.")
# return f"🌿 Identified as: {predicted_class}", info
# def predict(pil_image):
# return predict_image(pil_image)
# interface = gr.Interface(
# fn=predict,
# inputs=gr.Image(type="pil"), # Receive image as a PIL object
# outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Flower Information")],
# title="🌸 Flower Identification App",
# description="Upload an image of a flower to identify it and get care information."
# )
# if __name__ == "__main__":
# interface.launch()
# import gradio as gr
# import tensorflow as tf
# import numpy as np
# import json
# from tensorflow.keras.applications.efficientnet import preprocess_input
# from tensorflow.keras.preprocessing import image as keras_image
# # Load Model & Class Indices
# MODEL_PATH = "model.keras"
# CLASS_INDICES_PATH = "class_indices.json"
# FLOWER_INFO_PATH = "flower_info.json"
# def load_model():
# try:
# return tf.keras.models.load_model(MODEL_PATH)
# except Exception as e:
# print(f"Error loading model: {e}")
# return None
# def load_class_indices():
# with open(CLASS_INDICES_PATH, "r") as f:
# return json.load(f)
# def load_flower_info():
# with open(FLOWER_INFO_PATH, "r", encoding="utf-8") as f:
# return json.load(f)
# model = load_model()
# class_indices = load_class_indices()
# flower_info = load_flower_info()
# class_names = list(class_indices.keys())
# def preprocess_image(pil_image):
# # Convert PIL image to numpy array and preprocess
# img_array = keras_image.img_to_array(pil_image.resize((224, 224)))
# img_array = np.expand_dims(img_array, axis=0)
# return preprocess_input(img_array)
# def predict_image(pil_image):
# try:
# img_array = preprocess_image(pil_image)
# predictions = model.predict(img_array)
# predicted_class = class_names[np.argmax(predictions[0])]
# info = flower_info.get(predicted_class, "No additional information available.")
# return f"🌿 Identified as: {predicted_class}", info
# except Exception as e:
# return "Error in prediction", str(e)
# def predict(pil_image):
# return predict_image(pil_image)
# interface = gr.Interface(
# fn=predict,
# inputs=gr.Image(type="pil"), # Receive image as a PIL object
# outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Flower Information")],
# title="🌸 Flower Identification App",
# description="Upload an image of a flower to identify it and get care information.",
# theme="compact" # Optional: Use a compact theme to reduce UI clutter
# )
# if __name__ == "__main__":
# interface.launch(share=True, api=True) # Add api=True if you need API access
import gradio as gr
import tensorflow as tf
import numpy as np
import json
from tensorflow.keras.applications.efficientnet import preprocess_input
from tensorflow.keras.preprocessing import image as keras_image
# Load Model & Class Indices
MODEL_PATH = "model.keras"
CLASS_INDICES_PATH = "class_indices.json"
FLOWER_INFO_PATH = "flower_info.json"
def load_model():
try:
return tf.keras.models.load_model(MODEL_PATH)
except Exception as e:
print(f"Error loading model: {e}")
return None
def load_class_indices():
with open(CLASS_INDICES_PATH, "r") as f:
return json.load(f)
def load_flower_info():
with open(FLOWER_INFO_PATH, "r", encoding="utf-8") as f:
return json.load(f)
model = load_model()
class_indices = load_class_indices()
flower_info = load_flower_info()
class_names = list(class_indices.keys())
def preprocess_image(pil_image):
# Convert PIL image to numpy array and preprocess
img_array = keras_image.img_to_array(pil_image.resize((224, 224)))
img_array = np.expand_dims(img_array, axis=0)
return preprocess_input(img_array)
def predict_image(pil_image):
try:
img_array = preprocess_image(pil_image)
predictions = model.predict(img_array)
predicted_class = class_names[np.argmax(predictions[0])]
info = flower_info.get(predicted_class, "No additional information available.")
return f"Identified as: {predicted_class}", info
except Exception as e:
return "Error in prediction", str(e)
def predict(pil_image):
return predict_image(pil_image)
# Launch the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"), # Receive image as a PIL object
outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Flower Information")],
title="🌸 Flower Identification App",
description="Upload an image of a flower to identify it and get care information."
)
if __name__ == "__main__":
interface.launch(share=True) # No api=True here
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