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import gradio as gr |
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import matplotlib.pyplot as plt |
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import numpy as np |
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import PIL |
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import tensorflow as tf |
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model = tf.keras.models.load_model('model.h5') |
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class_name_list = ['Edible', 'Inedible', 'Poisonous'] |
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def predict_image(img): |
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img_4d = img.reshape(-1,224,224,3) |
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prediction = model.predict(img_4d)[0] |
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return {class_name_list[i]: float(prediction[i]) for i in range(3)} |
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image = gr.inputs.Image(shape=(224,224)) |
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label = gr.outputs.Label(num_top_classes=3) |
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title = 'Mushroom Edibility Classifier' |
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description = 'Get the edibility classification for the input mushroom image' |
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examples=[['app_interface/Boletus edulis 15 wf.jpg'], |
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['app_interface/Cantharelluscibarius5 mw.jpg'], |
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['app_interface/Agaricus augustus 2 wf.jpg'], |
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['app_interface/Coprinellus micaceus 8 wf.jpg'], |
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['app_interface/Clavulinopsis fusiformis 2 fp.jpg'], |
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['app_interface/Amanita torrendii 8 fp.jpg'], |
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['app_interface/Russula sanguinea 5 fp.jpg'], |
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['app_interface/Caloceraviscosa1 mw.jpg'], |
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['app_interface/Amanita muscaria 1 wf.jpg'], |
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['app_interface/Amanita pantherina 11 wf.jpg'], |
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['app_interface/Lactarius torminosus 6 fp.jpg'], |
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['app_interface/Amanitaphalloides1 mw.jpg']] |
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thumbnail = 'app_interface/thumbnail.png' |
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article = ''' |
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<!DOCTYPE html> |
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<html> |
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<body> |
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<p>The Mushroom Edibility Classifier is an MVP for CNN multiclass classification model.<br> |
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It has been trained after gathering <b>5500 mushroom images</b> through Web Scraping techniques from the following web sites:</p> |
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<br> |
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<p> |
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<a href="https://www.mushroom.world/">- Mushroom World</a><br> |
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<a href="https://www.wildfooduk.com/mushroom-guide/">- Wild Food UK</a> <br> |
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<a href="https://www.fungipedia.org/hongos">- Fungipedia</a> |
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</p> |
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<br> |
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<p style="color:Orange;">Note: <i>model created solely and exclusively for academic purposes. The results provided by the model should never be considered definitive as the accuracy of the model is not guaranteed.</i></p> |
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<br> |
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<p><b>MODEL METRICS:</b></p> |
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<table> |
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<tr> |
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<th> </th> |
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<th>precision</th> |
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<th>recall</th> |
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<th>f1-score</th> |
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<th>support</th> |
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</tr> |
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<tr> |
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<th>Edible</th> |
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<th>0.61</th> |
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<th>0.70</th> |
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<th>0.65</th> |
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<th>481</th> |
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</tr> |
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<tr> |
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<th>Inedible</th> |
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<th>0.67</th> |
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<th>0.69</th> |
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<th>0.68</th> |
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<th>439</th> |
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</tr> |
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<tr> |
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<th>Poisonous</th> |
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<th>0.52</th> |
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<th>0.28</th> |
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<th>0.36</th> |
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<th>192</th> |
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</tr> |
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<tr> |
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<th></th> |
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</tr> |
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<tr> |
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<th>Global Accuracy</th> |
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<th></th> |
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<th></th> |
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<th>0.63</th> |
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<th>1112</th> |
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</tr> |
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<tr> |
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<th>Macro Average</th> |
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<th>0.60</th> |
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<th>0.56</th> |
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<th>0.57</th> |
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<th>1112</th> |
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</tr> |
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<tr> |
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<th>Weighted Average</th> |
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<th>0.62</th> |
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<th>0.63</th> |
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<th>0.61</th> |
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<th>1112</th> |
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</tr> |
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</table> |
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<br> |
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<p><i>Author: 脥帽igo Sarralde Alz贸rriz</i></p> |
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</body> |
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</html> |
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''' |
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iface = gr.Interface(fn=predict_image, |
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inputs=image, |
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outputs=label, |
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interpretation='default', |
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title = title, |
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description = description, |
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theme = 'darkpeach', |
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examples = examples, |
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thumbnail = thumbnail, |
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article = article, |
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allow_flagging = False, |
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allow_screenshot = False, |
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) |
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iface.launch() |
